I rarely use this space to discuss game rules for the game I created, Baseball Trivia Challenge, but figured I’d make a rare exception. I sent the game to several folks to play test and have received some excellent feedback. The particular bit of feedback I want to discuss here concerns teams that enter the ninth inning trailing by a large number of runs. The reason this is a problem is because, regardless of their ability, players in Baseball Trivia Challenge cannot score more than 4 runs per at bat.

Both Earle Shamblin from Tabletop Baseball + and Nick Hawes came up with the same excellent house rule to deal with these situations. Their solution is to let the ninth batter continue the at bat as long as runs are scored. In other words, the at bat will continue unless one of three things happen: 1) the d20 die roll indicates no runs are possible; 2) the batting team’s manager answers the trivia question incorrectly; 3) the pitching team’s manager answers the trivia question correctly. It’s an innovative solution to a problem I didn’t recognize existed.

If you’ve read many of my posts you may have noticed that I’m a little over-the-top when it comes to mathematics and realism. So while I love their solution, I feel I must point out that the realism of the game will be affected if you implement this rule. Simply put, teams will score more runs than they should and relief pitcher ERAs could skyrocket. If that isn’t a problem for you, I think it’s the best solution to this problem because it puts the most pressure on both “managers.”

Another option that solves the realism issue is to use the Ninth Inning Rally Chart shown below and included with the game. I created it only after Earle and Nick pointed out the need for it, so they deserve most of the credit for it, too. You may use this chart whenever your team is down by no more than 9 runs in the ninth inning.* Simply state your intention to do so and roll the dice. Observe the rating of the player indicated (batter, pitcher, fielder) then locate the number in the cell corresponding to the player rating and the number of runs behind. If the d20 is less than or equal to the number shown, read the next trivia question to whosoever turn it is to answer. If you answer correctly or your opponent gets it wrong, you score the runs required to tie the game.

That’s all there is to it! Feel free to use either rule! Both approaches add a much-needed element of excitement to games that might otherwise be decided!

*Note that, in theory, you could use this chart to play an entire game where you selected how many runs you want to “try for” each at bat.

RTG	RUNS BEHIND
	1	2	3	4	5	6	7	8	9
0	—	—	—	—	—	—	—	—	—
1	1	1	—	—	—	—	—	—	—
2	2	1	1	1	—	—	—	—	—
3	3	2	1	1	1	1	—	—	—
4	4	2	1	1	1	1	1	1	—
5	5	3	2	1	1	1	1	1	1
6	6	3	2	2	1	1	1	1	1
7	7	4	2	2	1	1	1	1	1
8	8	4	3	2	2	1	1	1	1
9	9	5	3	2	2	2	1	1	1
10	10	5	3	3	2	2	1	1	1
11	11	6	4	3	2	2	2	1	1
12	12	6	4	3	2	2	2	2	1
13	13	7	4	3	3	2	2	2	1
14	14	7	5	4	3	2	2	2	2
15	15	8	5	4	3	3	2	2	2
16	16	8	5	4	3	3	2	2	2
17	17	9	6	4	3	3	2	2	2
18	18	9	6	5	4	3	3	2	2
19	19	10	6	5	4	3	3	2	2
20	20	10	7	5	4	3	3	3	2
21	20	11	7	5	4	4	3	3	2
22	20	11	7	6	4	4	3	3	2
23	20	12	8	6	5	4	3	3	3
24	20	12	8	6	5	4	3	3	3
25	20	13	8	6	5	4	4	3	3
26	20	13	9	7	5	4	4	3	3
27	20	14	9	7	5	5	4	3	3
28	20	14	9	7	6	5	4	4	3
29	20	15	10	7	6	5	4	4	3
30	20	15	10	8	6	5	4	4	3
31	20	16	10	8	6	5	4	4	3
32	20	16	11	8	6	5	5	4	4
33	20	17	11	8	7	6	5	4	4
34	20	17	11	9	7	6	5	4	4
35	20	18	12	9	7	6	5	4	4
36	20	18	12	9	7	6	5	5	4
37	20	19	12	9	7	6	5	5	4
38	20	19	13	10	8	6	5	5	4
39	20	20	13	10	8	7	6	5	4
40	20	20	13	10	8	7	6	5	4
41	20	20	14	10	8	7	6	5	5
42	20	20	14	11	8	7	6	5	5
43	20	20	14	11	9	7	6	5	5
44	20	20	15	11	9	7	6	6	5
45	20	20	15	11	9	8	6	6	5
46	20	20	15	12	9	8	7	6	5
47	20	20	16	12	9	8	7	6	5
48	20	20	16	12	10	8	7	6	5
49	20	20	16	12	10	8	7	6	5
50	20	20	17	13	10	8	7	6	6
51	20	20	17	13	10	9	7	6	6
52	20	20	17	13	10	9	7	7	6
53	20	20	18	13	11	9	8	7	6
54	20	20	18	14	11	9	8	7	6
55	20	20	18	14	11	9	8	7	6
56	20	20	19	14	11	9	8	7	6
57	20	20	19	14	11	10	8	7	6
58	20	20	19	15	12	10	8	7	6
59	20	20	20	15	12	10	8	7	7
60	20	20	20	15	12	10	9	8	7
61	20	20	20	15	12	10	9	8	7
62	20	20	20	16	12	10	9	8	7
63	20	20	20	16	13	11	9	8	7
64	20	20	20	16	13	11	9	8	7
65	20	20	20	16	13	11	9	8	7
66	20	20	20	17	13	11	9	8	7
67	20	20	20	17	13	11	10	8	7
68	20	20	20	17	14	11	10	9	8
69	20	20	20	17	14	12	10	9	8
70	20	20	20	18	14	12	10	9	8
71	20	20	20	18	14	12	10	9	8
72	20	20	20	18	14	12	10	9	8
73	20	20	20	18	15	12	10	9	8
74	20	20	20	19	15	12	11	9	8
75	20	20	20	19	15	13	11	9	8
76	20	20	20	19	15	13	11	10	8
77	20	20	20	19	15	13	11	10	9
78	20	20	20	20	16	13	11	10	9
79	20	20	20	20	16	13	11	10	9
80	20	20	20	20	16	13	11	10	9

A while back, I was involved in a fun discussion on the Tabletop Baseball+ Facebook group page about baseball game mechanics when a friend of mine posted the following: “When I’ve got a player who will ultimately hit .220, I will use him as much as I have to but certainly not in clutch situations, even though he was used that way during the season.”

It’s an intriguing point. As a kid playing replays in the late seventies and early eighties, I didn’t much care if the lineups for the teams I was playing were optimized to score the maximum number of runs, just as I didn’t care if the relief pitcher I called in from the bullpen was particularly skilled at his craft. My goal was not to win— I was managing both teams, after all— but rather to faithfully recreate games from that season. Indeed, without cable television (much less the Internet and Baseball Reference or Retrosheet to guide me), trying to settle on a lineup was hard enough!

That all changes when you’re playing to win. All of a sudden that .220 hitter who was relied on to perform in the clutch and didn’t (he did, after all, bat just .220) is no longer a viable option in those situations. Indeed, unless his defense is extraordinary, he is probably not the best choice to start, either.

In this article, I’ll consider position players who either started and put up rotten numbers as well as position players who did not start and put up extraordinary numbers and discuss ways to handle both.

For the most part, this discussion will be limited to dealing with these situations as they apply to one-offs or a short series of games (like you might encounter when playing in a tournament, for example), not an entire season.

We’ll start with the player whose playing time was limited but nonetheless put up monster numbers, a situation I like to refer to as “The Oscar Gamble Problem” because it first came to my attention when I was a kid playing Strat-O-Matic baseball with the 1979 card set.

The Oscar Gamble Problem: The Hot Hitting Reserve

For those too young to remember, Oscar Gamble was a journeyman outfielder who played for seven different teams during a 17-year major league career. By most accounts, Gamble, a lifetime .265 hitter, enjoyed his best year in the majors playing for the Chicago White Sox in 1977, when he finished 29th in the voting for American League MVP. But I can assure you his best season actually occurred two years later when he split time between Texas and New York and didn’t receive a single MVP vote.

Don’t take my word for it; check out the stats. In 64 games for Texas, Gamble managed to hit a career-best .335 (his previous best was .297 during the aforementioned 1977 season) and compiled enough extra-base hits to record a .522 slugging percentage. But even these gaudy statistics paled in comparison to those he compiled during his brief stint in The Big Apple later that same year, where he batted an incredible .389 and belted 11 homeruns in just 113 at bats*. His slugging percentage was .735 and his OPS a dazzling 1.187, stratospheric numbers shared by players named Ruth, Williams and Bonds.

For players of tabletop games at the time, this presented a real problem. Gamble wasn’t just a good backup outfielder for New York, he was easily their best player. If Jackson was, as he once claimed, “the straw that stirred the drink” in New York, Gamble was the drink. His statistics were clearly superior to Jackson’s and anyone else on the team for that matter. The decision to play him in left field over Piniella was a no-brainer, despite Piniella’s solid stats: .297 BA, 137 hits, and 69 RBI (third best on the team behind Jackson and Nettles).

In my experience, there are a couple of ways to deal with situations like this. One technique is to divide position players into three groups: starters, bench players, and emergency players and refer to the table below.

Pct. Games Played*	Description
60% to 100%	Starters. No restrictions on how they are used.
40% to less than 60%	Bench Players. Can be used as pinch-hitters and defensive replacements.
Less than 40%	Should be relegated to emergency situations only!

* You should feel free to adjust these percentages as you see fit.

Since Gamble played only 36 games for New York (22%), he will only be available in emergency situations (e.g., if and when Piniella is injured, etc.). Of course, you can play around with these percentages as you see fit but it’s a good idea to place at least some restrictions on these sorts of players. While less effective for Texas, in the game I created, Baseball Trivia Challenge, Gamble is worth more than 0.5 runs per game to the New York lineup, a figure that could result in as many as 10 additional wins over the course of a 162 game season or a critical victory in a 7-game series.

A second approach is to simply divide the number of games the player played by the number of games his team played during the season, convert that number to a d20 dice roll, and roll to see if the player is available before each game. For example, using this approach, Gamble would need to roll a 4 or less to be eligible to play.

While this approach will generally work to assure each player appears in the appropriate number of games, it isn’t particularly realistic, as players aren’t often shuffled in and out of the lineup every other game for no apparent  reason. Also, in the case of Oscar Gamble the main reason he missed so many games is because he wasn’t with the team until early October. As we’ll see throughout this discussion, there are no perfect solutions.

Next, we’ll consider the starter who compiled unimpressive numbers at the plate. For this example, we may as well consider the standard-bearer in this regard.

The Mendoza Line: What to do about Poor Hitting Starters

Mario Mendoza was a slick-fielding shortshop who struggled at the plate, compiling a lifetime .215 batting average. The term “Mendoza Line” was coined by his Mariner teammates, Tom Paciorek and Bruce Bochte, and was intended as a harmless joke. Today, it is frequently used to define the threshold of mediocre hitting, which is generally defined as a .200 average. If you’re below the Mendoza Line, you’re not long for “The Bigs”, or so the saying goes.

This is a harder case. The rules we discussed earlier to deal with hot-hitting reserves assures they are unlikely to be available to start in place of a weak-hitting starter, but do not prevent other players who played at least 60% of their team’s games from starting in his stead. For example, Larry Milbourne, who played 65 games at shortstop and was a better hitter, could be used to replace Mendoza for the ’79 M’s.

One approach to prevent this from happening is to assign starters and backups at each position based on games played.

In the case of Menoza and Milbourne, this approach would work well, since Mendoza played in 148 games and Milbourne only 65; however, in cases when the difference between the starter and backup is much smaller, it may not work as well if it works at all. For example, Leon Roberts played 67 games in left field for Seattle in 1979, but Tom Paciorek (47 games), John Hale (34), Dan Meyer (31) and Joe Simpson (27) all saw significant time at the position.

A second approach— to create d20 ranges for each player and roll to see who plays that game— has all the drawbacks mentioned earlier. In addition, it is not only a work intensive solution, it takes control away from the manager.

A third approach would be to utilize the d20 ranges described for “hot” players but instead of rolling to see who will play that game, roll to see who is “available” to play during the series or tournament. You could eliminate those players deemed “unavailable” entirely. Thus, it would be quite likely that Gamble would not only be unavailable to start, he would be unavailable period!

This situation is precisely the case described by my friend and is very difficult to deal with. In cases when the player is clearly in the lineup due to his defensive prowess, it is not unreasonable to conclude he is more valuable than other players at that position as a result. It isn’t unreasonable, but it may not be true. Indeed, I don’t believe it to be true in the game I created, where Mendoza is a much better fielder than Milbourne but fielding plays are relatively rare.

Part of the problem get’s to the heart of the issue my friend described: managers in a tabletop baseball game not only know the statistics each player compiled that year, but exactly how good they are according to the ratings in the game. For example, tabletop baseball game managers know that George Brett, who led the American League with a .329 average in 1990, batted just .255 the following year, but managers John Wathan and Hal McRae, who played with Brett and witnessed his brilliance, would have had no idea. Even if they assumed Brett wasn’t the .305 hitter he was over his lifetime, they might at least assume he was a .290 hitter or, at worst, a .280 hitter. But .255? Never!

If anyone has a great way of handling situations like this, post your ideas below!

* Starting in about 1973, Gamble consistently showed good power leading to above average slugging percentages. Near the end in his career in 1984, Gamble still managed to slug 10 homeruns in just 125 at bats despite hitting just .184.

 

When I was a kid, I played a lot of sports board games, from Strat-O-Matic (MLB baseball, NFL football, NBA basketball, NHL hockey and even a card and dice NCAA football game), to APBA (NBA basketball and Saddle Racing), to Statis-Pro (MLB baseball, NBA basketball). I played Speed Circuit (an under-rated game in my opinion), Avalon Hill’s USAC Auto Racing, Win, Place & Show, Paydirt… it’s a long list. In addition to all of that, I once played a World War II war game by Metagaming Concepts called “Hitler’s War.” I bought it because it was a “pocket game” and therefore not very expensive.

I am a huge fan of replays (even though I never made it past a few games when I played regularly as a kid) so usually when I played any of the sports games I owned I played solitaire and didn’t really care who won. I was much more focused on the stats, which I assiduously scribbled down on a scoresheet or, more often, a sheet of notebook paper.

Metagaming

Hitler’s War was different. I played against my uncle and brother and I really wanted to win. Indeed, I wanted to win so much I spent the night before preparing. And I don’t mean merely thinking through scenarios, I mean calculating the dice probabilities associated with various strategies. I was a “metagamer” before I even understood what that meant.

These days, I view metagamers and metagaming in a pretty negative light, even if I understand the desire to do it. (For the record, the game dragged on through the night and into the early morning, my early success rolling through Poland was halted and I grew irritable as my losses increased. So much for metagaming!)

Interestingly enough, a number of games seem to encourage it, including the Strat-O-Matic game company which publishes a baseball ratings guide

This article is all about metagaming. In it, I explore Strat-O-Matic’s pitcher fatigue system and compare the performance of a pitcher who has reached his point of weakness to his nominal performance when not fatigued. While I will utilize an equation or two here this is not intended to be anything more than a crude estimate.

This discussion will make more sense with an example so I will be referring to Randy Johnson’s 1995 Strat card throughout. Since I don’t have permission to include his card here, I’ll be sure to discuss only the relevant details.

Against right-handed batters, Johnson’s card includes two strikeouts that are changed to singles once he reaches his point of weakness. These occur in columns 4 and 6 and correspond to dice rolls of 7 and 9, respectively. The dice rolls are the same against lefties but occur in columns 5 and 6 respectively.

Now, I could spend a fair amount of time “reverse-engineering” Johnson’s card to show the probabilities associated with each result on his card and from these determine the probability he allows a walk, single, double, etc. I did a lot of that in preparation for this article to make sure the numbers I was calculating seemed reasonable. They did.

The mathematics of fatigue

Fortunately, trying to assess the effect fatigue has on his performance isn’t so complicated; we need only focus on the outs that get transformed to singles. To do so, we’ll need to consider how the probability of a right-handed batter getting a hit off Johnson increases when he is fatigued. This isn’t hard to do. The number of singles Johnson allows when fatigued that he would not have allowed otherwise is 6 + 4 = 10. This is due to the fact there are 6 chances of rolling a 7 and 4 chances of rolling a 9. This is from a total of 36 + 36 + 36 – 7 = 101 chances. Note that I am subtracting 7 in this case because Johnson allows a walk when a 7 is rolled in column 6 and walks don’t count as at bats and are thus ignored when calculating batting averages. At first glance, this would seem to indicate that Johnson allows opposing RH batters to bat 10/101 = .099 ≈ 100 points higher when fatigued. But this isn’t quite right.

Strat-O-Matic results are obtained from the pitcher’s card only half the time. In general, the formula looks like this:

BA_nominal = [(BA_b + BA_p) / 2]

BA_b is the batter’s batting average and BA_p is batting average allowed by the pitcher. (Note: These are not raw averages. They have been adjusted to ensure players will duplicate their real-life statistics when facing the same level of competition).

Here is how things look when Johnson has reached his point of weakness and is said to be fatigued.

BA_fatigued = [(BA_b + BA_p + 0.099) / 2] = (BA_b + BA_p) / 2 + .099 / 2 = BA_nominal + 0.099 / 2

The last bit is important. It shows that Johnson will allow opposing batters to hits approximately 50 points higher when fatigued. I might have just said that a few paragraphs ago— it seems intuitive—  but I feel it’s important to be deliberate in these cases since our intuition is sometimes wrong. (Note: The same holds true for left-handed batters, whom Johnson faced far fewer times).

Did Strat-O-Matic get it right?

Does Randy Johnson allow opposing hitters to bat 50 points higher when he is tired? We don’t really know. According to the data (which includes both left- and right-handed batters), Johnson’s performance drop-off occurs during innings 4 thru 6, not innings 7 thru 9, but we don’t know if he reached his point of weakness in any of the 23 games he lasted into the seventh inning or further; we only know that his Strat endurance factor inning is the seventh inning. Of course, with the statistics available today, it would possible (though tedious) to calculate how many innings he pitched in real-life when he would have been considered fatigued by Strat-O-Matic rules but I won’t bother to do that here since the point isn’t to show whether or not the point of weakness rule is realistic or not. I happen to like it and suspect a big reason for it is to encourage realistic usage, which I  support.

Table 1: Randy Johnson’s 1995 Performance By Inning

Split	G	IP	ERA	PA	AB	H	BB	SO	SO/W	BA	OBP	SLG	OPS
Innings 1-3	30	90	1.80	357	328	60	26	130	5.0	.183	.245	.265	.510
Innings 4-6	30	87.1	2.68	359	323	69	33	112	3.4	.214	.290	.337	.627
Innings 7-9	23	37	3.65	150	141	30	6	52	8.7	.213	.260	.312	.572

I did want to further explore how me might analyze the effects of fatigue on Johnson’s performance. We know that he will allow opposing batters to hit for a higher average but haven’t yet considered what that implies. I would like to know how many more runs he might be expected to allow under those circumstances. How much higher will his ERA be when fatigued?

The effect of fatigue on run-scoring

The table above provides an answer but it isn’t one I’d entirely trust. Most of Johnson’s stats are better in innings 7 thru 9 than they are in innings 4 thru 6 so it isn’t clear to me whether he was just extremely lucky to have allowed so few runs during those middle innings or simply unlucky in that regard in later innings. I’ll approach this question by tossing up a graph and a few equations that will make it seem like I’m doing real science when really all I’m doing is making a crude guess.

The graph below shows how a pitcher’s WHIP is related to runs scored during the 1995 American League season. Without delving into the details, I eliminated players with fewer than approximately 152 at bats and estimated runs scored using sabermetrics. Next, I plotted  the relationship between WHIP and runs scored (see below). It’s far from perfect but it provides at least a crude estimate. The linear regression equation in the white box on the right shows how changes in WHIP lead to more or less runs. For example, a 1.0 change in WHIP leads to 5.33 more or fewer runs.

Let’s try applying this equation to Johnson. Here are his statistics against right-handed batters in 1995:

Split	PA	AB	IP†	R	H	BB	BA	WHIP	R/G	Estimated R/G
vs RHB	754	688	186.2	54*	145	59	.211	1.093	2.60	3.14

* The number of runs allowed vs RHB (54) and the number of runs allowed vs LHB (7) don’t add up to the 65 runs Johnson allowed. Thus, the R/G could be between 2.60 and 2.80.
† The number of innings pitched were estimated by multiplying the proportion of plate appearances versus RHB by the total number of innings pitched, 214.1.

They suggest that the linear regression equation, applied to Johnson, overestimates the number of runs he should allow by anywhere from about a third to a half of a run per game. While this isn’t the purpose of the equation, it’s important to keep in mind we’re pouring various quantities into beakers and test tubes, not performing real science.

So how do things change when Randy Johnson is fatigued? Since the only thing that changes is the number of hits (specifically singles) Johnson allows, that is all we’ll calculate: (.099 ÷ 2 × 688) ≈ 33.7 additional hits allowed. This produces a marginal WHIP rating of approximately 0.181. Since 0.181 × 5.3302 ≈ 0.96 runs per game, we conclude that Johnson’s ERA will be nearly a run higher when fatigued, an estimate that compares favorably with the actual data, which suggests a 0.97 rise in ERA from innings 4-6 to innings 7-9 (refer to Table 1 above).

Much Ado about nothing?

It obviously isn’t necessary to expend this sort of effort calculate a pitcher’s estimated performance when fatigued, but I wanted to provide a rough assessment of how fatigue effects even a pitcher of Johnson’s caliber and, as we observed, the effects are considerable. Beyond that, knowing how a pitcher performs when tired is really only useful when comparing him to relief pitchers who may replace him, and for this, it is probably enough to glace at their respective cards, noting that Johnson’s strikeouts will count as hits.

 

A while ago I replayed the 1986 World Series between New York and Boston made famous by Bill Buckner’s infamous error that would have given Boston the victory in six games. Looking back at the box score, I was surprised to learn Buckner’s blunder wasn’t the only error committed by Boston that night— catcher Rich Gedman and right fielder Dwight Evans were both charged with errors, as well. Evans’ throwing error on a Mookie Wilson single in the fifth inning allowed Ray Knight to advance to third base; he later scored on a ground ball double play. New York also made two errors.

In the deciding game, Boston led 3-0 after five innings before New York exploded for 8 runs over the course of the next three innings and won handily, 8-5.

In my replay, I decided a 7-game series just wouldn’t do. I decided to play 40 games!

Not surprisingly, the series was close, with New York winning the series 21 games to 19. After being badly outplayed in the early games of the series, victory was made possible by an improbable run of 10 wins in 12 games which began with a narrow 3-2 win in game sixteen.

Interestingly, if the series had been just seven games, Boston would have dominated, winning in 5 games: 5-0, 6-3, 3-4, 4-2, and 4-1.

Series Stats

1986 New York (N) Batting

Name	G	AB	RN	R/G
Wally Backman	38	39	26	.667
Keith Hernandez	40	44	26	.591
Ray Knight	40	41	25	.610
Lenny Dykstra	39	33	21	.636
Gary Carter	36	37	20	.541
Rafael Santana	40	41	14	.341
Howard Johnson	20	21	12	.571
Darryl Strawberry	36	35	10	.286
Kevin Mitchell	14	15	8	.533
pitcher	20	20	3	.150
Mookie Wilson	19	19	3	.158
Lee Mazilli	4	5	2	.400
Ed Hearn	4	4	1	.250
George Foster	8	8	0	.000
Tim Teufel	2	2	0	.000
TOTALS	360	364	171	.470

1986 New York (N) Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Bruce Berenyi	1	0	0	0	0	0	1	0	0	0.00
Roger McDowell	16	0	0	1	1	7	25	3	3	1.08
Bob Ojeda	7	7	1	5	0	0	48	13	9	1.69
John Mitchell	1	1	0	0	0	0	3	1	1	3.00
Randy Meyers	3	0	0	1	0	0	3	1	1	3.00
Dwight Gooden	8	8	3	4	1	0	59	21	20	3.05
Rick Aguilera	8	8	0	3	2	0	51	26	22	3.88
Ron Darling	8	8	3	2	4	0	54	26	26	4.33
Sid Fernandez	8	8	1	2	4	0	51	31	28	4.94
Doug Sisk	12	0	0	3	1	1	18	11	11	5.50
Randy Niemann	7	0	0	0	2	0	15	10	10	6.00
Rick Anderson	7	0	0	0	0	0	13	11	11	7.62
Jessie Orosco	15	0	0	0	4	2	25	23	22	7.92
TOTALS	101	40	8	21	19	10	366	177	164	4.03

1986 Boston Batting

Name	G	AB	RN	R/G
Wade Boggs	39	43	43	1.000
Jim Rice	40	38	25	.658
Tony Armas	40	41	24	.585
Dwight Evans	39	41	21	.512
Bill Buckner	39	40	19	.475
Marty Barrett	40	42	15	.357
Rich Gedman	39	39	11	.282
Ed Romero	11	11	6	.545
Don Baylor	20	16	4	.250
Rey Quinones	20	20	4	.200
Spike Owen	9	10	3	.300
pitcher	20	20	2	.100
Marc Sullivan	2	2	0	.000
Steve Lyons	1	1	0	.000
Dave Stapleton	1	1	0	.000
Mike Greenwell	1	1	0	.000
TOTALS	361	366	177	.484

1986 Boston Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Calvin Schiraldi	8	0	0	1	0	0	9	0	0	0.00
Sammy Stewart	9	0	0	1	1	0	14	1	1	0.64
Mike Brown	6	0	0	2	0	1	12	2	2	1.50
Joe Sambito	8	0	0	0	0	0	12	3	3	2.25
Oil Can Boyd	8	8	5	2	3	0	64	26	22	3.09
Bruce Hurst	7	7	3	1	4	0	53	20	20	3.40
Roger Clemens	9	9	4	6	3	0	70	29	27	3.47
Steve Crawford	8	0	0	1	0	0	11	7	5	4.09
Tom Seaver	5	5	2	1	3	0	35	17	17	4.37
Tim Lollar	2	0	0	0	1	0	2	1	1	4.50
Al Nipper	6	6	1	4	1	0	39	24	22	5.08
Jeff Sellers	5	5	0	0	3	0	30	29	24	7.20
Bob Stanley	8	0	0	0	2	0	13	12	12	8.31
TOTALS	89	40	15	19	21	1	364	171	156	3.86

 

 

One of the folks I watch on YouTube from time to time is genial guy named Chris, aka Strat-O-Matic Delaware (you can check him out here). He is currently immersed in a project he calls Project 61: Chase for the Babe. Fortunately, Chris is not 61-years-old and a fan of baseball so this isn’t the sort of “project” that will be used as evidence in a later sexual harassment trial. The chase, in this case, is for Babe Ruth’s 1927 home run record and the 61 refers to both the year the record was broken and the number of home runs it took to break it.

You will, of course, recall it was Roger Maris who broke Ruth’s long-standing record and that his accomplishment was met with equal parts delight and derision. There have been countless books and articles written about the chase and even a Hollywood movie.

Chris’ replay is not centered around the human drama surrounding the chase. As he describes it, it is a “no frills race to the top.” As was true in 1961, Chris is focusing on both Maris and Mantle, who, in real-life, entered September on nearly even terms with 51 and 48 home runs, respectively.

Chris’ project caught my attention about the same time I was monitoring another Strat-O-Matic replay by another affable fellow named Eric from Higher Ground Gaming (check him out here). He recently finished a computer replay of the 1978 Boston Red Sox and I have to say the results were impressive. The home run king in his replay was Jim Rice, who belted 46 round-trippers for Boston that year. In the replay, Rice hit 53.

The accuracy of his results made me think of the project Chris is engaged with. It made me wonder: What are the chances that Chris finishes the replay and neither Maris or Mantle break Babe’s record? What are the chances they both break it? I wondered, too, what the chances were that either simply obliterated Ruth’s mark.

What are the odds?

So I sat down in front of my computer, opened Excel and did what I often do in these situations: I decided I was suddenly tired, walked over to the couch and fell asleep. (Obviously I eventually woke up and returned to the task at hand).

I calculated that Maris stands about a 52% chance of breaking the record while Mantle’s chances are below the Mendoza Line at 18%. The probability of both players failing to exceed 60 home runs is about 40%. I’ll explain how these numbers were calculated in a minute. The results didn’t surprise me, but they also didn’t prepare me for other surprises to come. I’ll get to that later. But first I promised to discuss the methodology I used to calculate them.

Note: I feel I owe it to folks to show how I arrived at these numbers, in the off chance they care. Feel free to skip to the end of this article where I discuss why I found these number interesting in the first place.

A bunch of math…

The probabilities above and those we’ll utilize for this discussion will be calculated in two ways. The first is purely mathematical. I will treat both Maris’ and Mantle’s plate appearances as a series of Bernoulli (or binomial) trials. (If you need to leave your computer or phone to grab a quick nap, go for it! I understand!) Without going into detail, this approach assumes that both Maris and Mantle will hit home runs at a fixed rate or probability. I determine these probabilities by dividing the number of home runs each player hit by their number of place appearances. For example, since Maris hit 61 home runs in 698 plate appearances, his home run probability is calculated as 61 ÷ 698 ≈ 0.0874 ≈  8.74%. In a similar fashion, I calculate Mantle’s home run probability to be 54 ÷ 646 ≈ 0.0836 ≈  8.36%. (Note: You could just as easily use at bats; it makes little difference).

Looking at these numbers it might surprise you that Maris is so much more likely to break Ruth’s record than Mantle but don’t forget Maris had 52 more plate appearances. When I calculated each player’s home run totals it was based on their number plate appearances. Focusing on just Maris for the time being, I used Excel to calculate the probability he will hit 61 or more home runs as follows:

= 1 - BINOM.DIST(60,698,61/698,TRUE)

You may already have noticed a major problem with my approach: it assumes Maris’ probability of hitting a home run doesn’t change from at bat-to-at bat, much less game-to-game, which simply isn’t true†. While I won’t try to model each at bat, we can use Maris’ actual game-by-game results to calculate these probabilities, as well. This is the second method.

In an earlier article about the 50/50 model utilized by Strat-O-Matic and other games, I relied exclusively on the results of simulations to make my point when, strictly speaking, it wasn’t necessary. None of the underlying mathematics was very complicated. Simulations can be invaluable in cases when the underlying probability distributions get even remotely complicated, as is the case here. For this reason, I’ll treat Maris’ game-by-game results as the population data then create a number of “bootstrap” samples to use in our analysis.  Put another way, I’ll simulate 1 million 1961 seasons and see how his home run totals vary.

To do, this I will consider his home run probability on a game-by-game basis. I will essentially ignore those games he didn’t hit a home run since the probability is zero for those cases. For example, Maris did not hit a home run until the eleventh game of the 1961 season, a 13-11 victory over Detroit. Since he had 5 plate appearances and 1 home run in this game, I will simulate this game by giving him a 1-in-5 chance to hit a home run for each plate appearance, then record the number of home runs he hit (which can be any integer from 0 to 5).

These results are compared to the simple model using the binomial distribution in the table below.

Home runs	Binomial Model	1,000,000 Trials
50 or less	7.7%	5.5%
60 or less	48.1%	47.4%
61 or more	51.9%	52.6%
65 or more	31.4%	29.6%
70 or more	12.8%	10.2%
74 or more	5.0%	3.2%

Not surprisingly, the two approaches produced similar results. Here are the results for Mantle.

Home runs	Binomial Model	1,000,000 Trials
50 or less	31.5%	28.8%
60 or less	82.3%	85.3%
61 or more	17.7%	14.7%
65 or more	7.1%	4.7%
70 or more	1.6%	0.7%
74 or more	0.4%	0.1%

Finally the stuff I want to talk about!

I have taken us on a very long and circuitous route to the facts I find most interesting. Let’s start with Maris. There is a roughly 12% chance that Maris will hit 70 or more home runs, which is about the same probability as rolling a sum of 9 on two dice in Strat, not exactly a rare occurrence. And yet, I wonder how many people would look asconce at those sorts of results. How many people would find them unrealistic?

Even more extreme, there is about a 5% chance that Maris will wind up with 74 or more home runs, breaking Bonds’ future mark of 73. A 5% chance is like rolling a 3 or, put another way, the difference between Mario Mendoza and an average big league hitter. Roll a 4, and that’s about the same chance both players have of breaking Ruth’s record.

These thoughts crossed my mind when Eric (see above) was summarizing the results of his 1978 Boston Red Sox replay. Overall I thought the results looked great. But Rice hit 53 home runs when he should have hit 46 and I remember thinking I wish he’d hit 49.

It will be interesting to see how Chris’ project turns out. I don’t think Maris will reach 70 home runs. But I’ll know not to be too surprised if he does.

† Note: Neither model is as complex as Strat-O-Matic which not only models every at bat against a wide variety of pitchers but also accounts for lefty/righty and righty/lefty match-ups. In addition, there is no guarantee that the number of plate appearances will be the same in Strat-O-Matic as in real life. Indeed, we should expect Strat results that are less predictable with more variance than the numbers here.

I mentioned earlier I first got into baseball in 1979, about the same time I bought my first Strat-O-Matic baseball game that included the 1978 season. One of the teams I enjoyed playing with was the Houston Astros. One reason for that was a pitcher I’d never heard of before: J.R. Richard. Although I’ve long since parted with my 1978 (and 1979) card sets, I recall those 303 strikeouts were featured prominently on his 1978 card. I suspect the 141 walks were there, too, but I don’t really remember them. 

He as even better in 1979, fanning 313 batters while decreasing his number of walks to under 100. Houston finished second in the NL West that near, after finishing fifth the year before. Things were looking up for the team, and Richard was at the heart of it.

He was in the midst of his best season ever in 1980— and the team was, too— when disaster struck.

On July 30, 1980, Richard was warming up for a game against Philadelphia when he suddenly collapsed, the victim of a massive stroke. Although he would try, he never returned to major league baseball.

His life after baseball took a few more unexpected twists— including a period of time when he was homeless on the streets of Houston— but as was true of his pitching career, he found his groove again. I hadn’t been aware of his struggles. I’m pleased he persevered.

In 2015 he wrote a book called Still Throwing Heat chronicling his life from his time in baseball to the present day. I’ve included a picture of the cover. You can buy it on Amazon if you’re interested.

In part due to Richard (and, I must admit, also due to their unique uniforms), I decided to rate two of the teams he played on: the 1978 team and the 1980 team that lost the NLCS to the eventual World Series Champion Philadelphia Phillies. The same Phillies team Richard was due to face when his major league career came to a halt.

As fate would have it, Houston faced the Phillies in the NLCS later that year. The NLCS was only five games in 1980 (it wouldn’t be expanded to seven games until 1985). Houston jumped to a 2-1 lead then lost consecutive extra-inning affairs, allowing Philadelphia to advance to the World Series against Kansas City. No less an authority than The Sports Encyclopedia: Baseball called the 1980 NLCS “the most exciting playoff series ever staged.” Richard, of course, was no longer with the team.

I thought it might be fun to pit them against Philadelphia but with Richard on the roster to see if it changed things. It did. You can read all about it below.

Game 1: Richard outduels Carlton!

Joe Morgan scored 2 runs in the eighth inning to give the Astros a 4-3 lead and closer, Joe Sambito, shutdown slugger Mike Schmidt in the ninth to give Houston a 4-3 win and 1-0 lead in the best-of-seven series.

The game featured two of the best pitchers in the game. J.R. Richard pitched 8 innings and held Philadelphia to just 3 runs, all unearned. Phillies ace, Steve Carlton, pitched well in defeat.

	1	2	3	4	5	6	7	8	9	F
Houston	0	2	0	0	0	0	0	2	0	4
Philadelphia	0	0	0	3	0	0	0	0	0	3
W – J.R. Richard (1-0)			L – Steve Carlston (0-1)				Sv – Joe Sambito (1)

Game 2: Late rally leads Philadelphia past Houston, 5-3!

Bake McBride and Mike Schmidt led a late rally that pushed Philadelphia past Houston to tie the series at one game a piece.

Joe Niekro started for Houston and pitched 8 quality innings, allowing just 3 runs. Reliever, Joaquin Andujar gave up the game winning runs to Schmidt in the ninth inning and was tagged with the loss.

	1	2	3	4	5	6	7	8	9	F
Houston	0	0	1	0	0	2	0	0	0	3
Philadelphia	0	0	0	0	0	1	0	2	2	5
W – Kevin Saucier (1-0)			L – Joaquin Andujar (0-1)

Game 3: Ryan and bullpen shutout Phillies!

Nolan Ryan pitched 5 scoreless innings before giving way to a strong performance from the Houston bullpen that included a surpise save from 23-year-old, Gordie Pladson, who finished the regular season 0-4 with a 4.35 ERA.

Meanwhile, Bob Walk (11-7 with a 4.57 ERA during the regular season) was the tough luck loser despite pitching 6 innings and allowing just 1 run.

Jose Cruz scored the game-winner for Houston.

	1	2	3	4	5	6	7	8	9	F
Philadelphia	0	0	0	0	0	0	0	0	0	0
Houston	1	0	0	0	0	0	0	0	X	1
W – Nolan Ryan (1-0)			L – Bob Walk (0-1)				Sv – Gordie Pladson (1)

Game 4: Forsch, Houston shutout Phillies … again!

Ken Forsch pitched a complete game gem, shutting out the dangerous Philadelphia lineup for a second straight game and even contributing on offense by scoring the game-winning run in the fifth inning. Rafael Landestoy and Joe Morgan added one run each in the seventh and eighth inning to account for the final margin.

	1	2	3	4	5	6	7	8	9	F
Philadelphia	0	0	0	0	0	0	0	0	0	0
Houston	0	0	0	0	1	0	1	1	X	3
W – Ken Forsch (1-0)			L – Larry Christensen (0-1)

Game 5: It’s a wrap! Richard propels Houston to 4-1 series win!

J.R. Richard and Joaquin Andujar combined to shutout the slumping Phillies for the third game in a row and Houston won 5-0 to win the NLCS four games to one. Philadelphia could do nothing right and Houston little wrong in handing Steve Carlton his second loss of the series.

Over the course of 5 games, the Phillies tallied just 8 runs to Houston’s 16.

	1	2	3	4	5	6	7	8	9	F
Houston	2	0	0	0	0	3	0	0	0	5
Philadelphia	0	0	0	0	0	0	0	0	0	0
W – Ken Forsch (1-0)			L – Larry Christensen (0-1)

Recap

J.R. Richard had a fantastic series, pitching 15 innings while allowing no earned runs. Even so, it would be hard to give him all the credit for the victory over Philadelphia. Simply put, the Phillies didn’t play well, averaging just 1.6 runs per game and going 27-straight innings without scoring a run.

Except for game two, Mike Schmidt, who slugged 48 homeruns in the first of what would be two consecutive MVP seasons, failed to produce in critical situations and no one else on Philadelphia did either.

Houston, hardly an offensive juggernaut, received solid contributions from catcher Alan Ashby (5 runs), Jose Cruz and Joe Morgan (3 runs each) but, of course, it was the pitching staff that shined, compiling a remarkable 1.00 ERA for the series. Take away Richard, and their ERA was still just 1.50 (their ERA was 3.49 during the actual series).

It’s worth noting that both Houston and Los Angeles, who finished the season tied with 92-70 records, finished slightly ahead of Philadelphia, who tallied 91 wins against 71 losses. Indeed, the Astros entered their final series against LA with a 92-67 record, lost three straight, then needed an extra game to settle the tie.

The series may not have proved that Richard would have been enough to lead them to victory, but it did confirm that Houston was a good team in 1980 and may well have been the Phillies equal.

One of the complaints I hear most often about baseball board games like Strat-O-Matic is that they utilize a 50/50 model. This is a game mechanic where the result of the at bat is randomly read from the batter’s card half the time and the pitcher’s card the other half. Thus, the classic batter vs. pitcher duel isn’t a duel at all; it’s only ever influenced by one player or the other (and, of course, the occasional fielder).

Growing up, this bothered me, too. In fact, it bothered me most whenever I played Statis Pro Baseball and the pitcher on the mound had a large PB range (e.g., 2-9). I would look longingly at all the doubles and homeruns available on the batter’s card and know the moment I read the next fast action card and realized the result would come from the pitcher’s card they were all for naught.

I noticed it less in Strat-O-Matic because it utilized the 50/50 model and at least that seemed fair. Nevertheless, in critical situations my favorite pitcher or batter was reduced to being a mere spectator.

These days I don’t play board games very often but out of a sense of nostalgia in part driven by the work I’ve put into my own game, Trivia Challenge Baseball, I’ve been watching a lot of YouTube videos produced by folks who do and a few of them complain about it, too— or at least tout those games that include batter-pitcher interactions for each at bat as vaguely superior in this regard.

It got me thinking: does it really matter? The first thing I thought was that if you take a Strat-O-Matic batter and pitcher card and tape them together, you essentially have a single card where the pitcher and batter both influence the results of the at bat. Certainly no one would argue it wouldn’t produce the same results the two cards produced separately before they were taped together.

Of course, some might argue the 50/50 model is still in place in this example, since that batter columns are still 1 thru 3 and the pitcher columns 4 thru 6.

If we wanted to, we could cut out every dice roll result from the pitcher and batter cards and randomly assign them to a column (1 thru 6) on a new card. Indeed, we could physically create this sort of card with a pair of scissors, glue and the grotesque indifference required to defile Strat-O-Matic cards! Such a Frankenstein-like card, when constructed, would also produce the same results. None of the probabilities would have changed.

This would seem to suggest that the influence the pitcher has on the batter and vice-versa is still present in a 50/50 model and that our belief it isn’t is simply a misconception on our part.

If you aren’t buying any of this, that’s OK. Being skeptical is a good thing. So let’s explore things further. We’ll start with a definition: When are two sets of results the same?

I would posit they are the same if they both produce the “same” distribution and measures of central tendency, including streaks. To see what I mean, let’s consider a simple example: a batter’s hits. A hit, of course, is anything from a single to a homerun. I’ll assign a 1 to every hit and a 0 to every out. We’ll consider the following two sets representing 10 at bats:

Set 1 = {1, 0, 0, 1, 0, 0, 0, 1, 0, 1}

Set 2 = {1, 1, 1, 0, 0, 0, 0, 0, 1, 0}

Obviously a set of 10 outcomes is on the small side but don’t worry, I’m not making a scientific point here. Right now, I’m just trying to settle on a definition of “same.”

Note that both sets have the same mean and variance. They have the same mode and median. But they don’t quite look the same. Set 2 appears more “streaky”. When I think of the 50/50 model, I am convinced the mean is the same as the mean obtained from samples involving the combined contributions of the batter and pitcher, but I wonder about streaks.

To explore this further, imagine a .400 hitter facing a pitcher who allowed batters to hit just .150. (Though slightly more extreme, this is a little like pitting Ted Williams from 1941 against Pedro Martinez from 2000 when Ted batted .406 and Pedro allowed opposing hitters to bat just .167). Let’s assume the league average is .250. Thus, for our .400 hitter to bat his average in a 50/50 model, his card would need to represent a .550 average (since .550 × ½ + .250 × ½ = .400). Likewise, our superstar pitcher would allow just a .050 average on his card (.050 × ½ + .250 × ½ = .150). When facing each other, we expect our mythical batter to hit .300, since  .550 × ½ + .050 × ½ = .300.

I chose to use these fictional players for this example precisely because their cards are so different. I think intuitively we might be tempted to believe our .400 batter will be less streaky in a 50/50 model where his probability of getting a hit goes from .550 to .050 depending on whose card is read.

To test this theory, I’ll run a simulation where our .400 batter faces off against our superstar pitcher over a series of 100 million at bats. We will record the number of hits our batter accumulates over those at bats and calculate a few statistics. Note the combined results referred to below are those compiled by a .300 hitter while the split results are those obtained by randomly reading each result from the batter or pitcher card (i.e., our .400 batter and .050 pitcher).

Combined Results	Statistic	Split Results
.3001	Mean	.3001
6,303,213	2 Hit Streaks	6,303,685
1,894,404	3 Hit Streaks	1,892,426
568,379	4 Hit Streaks	566,823
170,349	5 Hit Streaks	169,673
51,232	6 Hit Streaks	51,198
15,470	7 Hit Streaks	15,332
4,610	8 Hit Streaks	4,611
1,373	9 Hit Streaks	1,355
457	10 Hit Streaks	405

* Note: Steaks are defined as consisting of exactly the number of hits shown. For example, every 3 hit streak includes a 2 hit streak but these were not counted as 2 hit streaks. Put another way, all streaks are bracketed by outs.

Not surprisingly, the means are the same rounded to 4 decimals and very close to the .300 average we expected (which isn’t a surprise given the size of the sample). Even more encouraging are the streaks, which are very similar between the two sets.

If you’re the meticulous type and looked closely at the numbers in the table, you may have noticed the combined results seem to include more streaks, even though the sums are close. There are a couple of items worth noting. First, this was not true of other data sets I ran. Second, remember we are viewing the raw totals—not the percentages— and they tend to accentuate differences between the two sets.

The Law of Large Numbers governing probabilities does not imply differences will vanish as the sample size increases, only that the average (or mean) will tend to get closer to the expected value as the sample size increases. The next two graphs provide evidence of this fact. The first graph shows that the difference between the expected number of hits and the number of hits observed appears to be growing while the second graph shows how the difference between the calculated batting average and the expected batting average (.300) seems to be shrinking.

So where does this leave us? Well, for one thing, it seems to indicate that on an at bat-by-at bat basis the results from a 50/50 model are indistinguishable from those produced by models that simultaneously account for the pitcher and batter. This is a powerful statement and should be enough to end the argument.

And yet…

Perhaps, like me, you’re having a hard time getting over the fact that individual at bats are controlled by a single player. If so, I urge you to read the above paragraph again and let it sink in. We aren’t talking above averages or long term trends. If you take any set of results from a 50/50 model— including sets with just one, two or three at bats— you won’t be able to tell which model produced it.

The truth is, the batter-pitcher interactions do exist for every at bat in the 50/50 model and I can prove it. It’s the little white die you roll in Strat-O-Matic, for example, to determine which card to read. It may not look or feel like the sort of interaction you get with, say, Payoff Pitch Baseball or Replay Baseball, for example, but it’s there and produces the same outcomes.

I’ve identified the interactive mechanism but I said I’d prove it and I haven’t yet. I’ll do so by running another simulation featuring our two ballplayers, only this time I won’t roll a little white die to determine whose card to read, I’ll simply alternate between the two cards. In other words, I will use a “deterministic” 50/50 model.

Looking at the results, it is clear at a glance that the deterministic model does not produce the same results. Notice the means are the same but nothing else. It produces the kind of results critics of a 50/50 model would be correct to criticize. But, as we’ve seen, these aren’t the results 50/50 models produce.

Combined Results	Statistic	Split Results
.3000	Mean	.3000
6,298,221	2 Hit Streaks	1,924,607
1,889,300	3 Hit Streaks	748,722
567,063	4 Hit Streaks	52,790
169,950	5 Hit Streaks	20,544
51,095	6 Hit Streaks	1,436
15,234	7 Hit Streaks	578
4,493	8 Hit Streaks	31
1,358	9 Hit Streaks	12
419	10 Hit Streaks	0

Still not convinced? How about we roll for it!

 

In 2004, Boston broke “The Curse of the Bambino”. The notion that Boston was somehow “cursed” arose from the failure of the Boston team to win a World Series after selling Babe Ruth to New York after the 1918 season. Prior to that time, Boston had won the World Series five times from 1903 to 1918.

Mind you it wasn’t until after the publication of the book The Curse of the Bambino by Dan Shaughnessy in 1990 that the term really entered the baseball lexicon. Shaughnessy did baseball fans a favor. Until that time the trading of Ruth to New York was often referred to by Boston fans as “Remember that time when that idiot Boston owner, Harry Frazee, sold the greatest player in baseball to New York for $100,000 and the New York went on to appear in seven World Series and win four of them from 1920 to 1934 while Boston finished no better than fourth in the American League?” (Note to reader: I may have made this up. I can only imagine this was the reaction of Boston fans!)

Whether you believe there was a curse or not, selling a player of Ruth’s caliber certainly didn’t help Boston during his playing days. That said, Ruth was part of a larger movement of talent out of Boston. Indeed, after winning three World Series with Boston, the team finished a dismal sixth in the American League in 1919, his last year with the team.

And it must be also be noted that this same team failed to win a World Series with the great Ted Williams, though they came close in 1946.

In any event, 2004 was a watershed year for Boston. Finishing 98-64, three games behind New York for the Eastern Division title, Boston beat Anaheim in the ALDS to set up a championship matchup with New York. Boston promptly lost three games in a row— the third game 19-8 in Boston— to go down 0-3 in the series. They rebounded with two gritty extra-inning wins at home before returning to New York, where they won 4-2 and 10-3 in the final.

Boston may not have been a great team but they had plenty of offensive weapons. Manny Ramirez led the way with 43 homeruns and 130 RBI but David Ortiz, affectionately known as “Big Papi” to fans, was not far behind, banging our 41 homeruns and 139 RBI. Center fielder Barry Gibb, who had several hit records with the Bee Gees in the late seventies … err, I mean center fielder Johnny Damon banged out 189 hits and added 94 RBI while catcher Jason Varitek and infielders Mark Belhorn and Kevin Millar were all solid at the plate. Indeed, Boston averaged an incredible 6.38 runs a game at Fenway and 5.86 runs per game overall.

On the mound, the Red Sox were dynamite, able to hit opponents with a 1-2 pitching punch that featured Curt Schilling and Pedro Martinez. The 37-year-old Schilling finished the year 21-6 with a 3.26 ERA, good enough for second in the Cy Young Award voting. Martinez, on the other hand, suffered through a down year by his standards. After seven consecutive years with an ERA well below 3.00, Pedro posted a 3.90 ERA but still managed to win 16 games and tally 227 strikeouts, second best in the American League. A down year by Pedro is a very good year for everybody else.

Led by Keith Foulke, who recorded 32 saves, Mike Timlin and Alan Embree, Boston boasted a solid bullpen that could be relied on to keep them in games.

In the World Series, the now red hot Sox would face a St. Louis team that had steamrolled through the National League in 2004, winning 105 games against just 57 losses. St. Louis was loaded with offensive talent.

Albert Pujols batted .331, hit 46 homeuns and drove in 123 runs. Center fielder Jim Edmonds was nearly as good, batting .301 with 42 homeruns and 111 runs batted in. And yet it was 29-year-old third baseman Scott Rolen who led the team with 124 RBI. Rolen, too, could hit for power, swatting 34 round-trippers, while batting .314. The hit parade continued with John Mabry (.296, 13 HR) and Tony Womack (.307 BA). Not surprisingly, St. Louis averaged 5.28 runs per game, tops in the NL.

St. Louis boasted fewer stars on the mound but, together, they were effective, posting a major league best 3.74 ERA. Chris Carpenter lead  all starters in winning percentage, ERA and strikeouts.

The bullpen was very good. Jason Isringhausen recorded 47 saves while bullpen mates Cal Eldred (3.76 ERA), Julian Tavarez (2.38 ERA), Ray King (2.61 ERA) and Steve Kline (1.79 ERA) all logged a near-equal number of innings while posting low ERAs.

Series Outlook and Predictions

Despite having won fewer games, Boston matches up well against St. Louis. Pitching in a very hitter-friendly park, Boston’s pitching staff outperformed the St. Louis staff.

The reverse was true at the plate. The Boston batters benefitted from playing half their games at Fenway, where they averaged over a run more per game than on the road. On paper at least, this looks like another 6- or 7-game series in the offing. As for the winner: flip a coin!

---

Game 1: Boston strikes first!

Johnny Damon scored 3 runs in the second inning to give Boston a lead they would never relinquish. Meanwhile, starter, Curty Schilling, held St. Louis scoreless for five innings, maneuvering deftly through the treacherous early part of the St. Louis lineup that included Albert Pujols and Scott Rolen. By the time Tony Womack scored for St. Louis in the top of the eighth inning, Boston already led 5-0.

	1	2	3	4	5	6	7	8	9	F
St. Louis	0	0	0	0	0	0	0	2	0	2
Boston	0	3	0	0	0	0	2	0	X	5
W – Curt Schilling (1-0)			L – Matt Morris (0-1)				GW – Johnny Damon (1)

Game 2: Marquis shuts down Boston; St. Louis rolls, 6-1!

Johnny Damon scored the only run for Boston in the second inning while St. Louis received 3 runs each from John Mabry and Edgar Renteria to win going away, 6-1. Jason Marquis pitched 6 strong innings for St. Louis to earn the win. Pedro Martinez was uncharacteristically bad for Boston, lasting just 6 innings and allowing 3 earned runs.

	1	2	3	4	5	6	7	8	9	F
St. Louis	0	0	3	0	0	0	3	0	0	6
Boston	0	1	0	0	0	0	0	0	0	1
W – Jason Marquis (1-0)			L – Pedro Martinez (0-1)				GW – John Mabry (1)

Game 3: Walker wins it!

After Boston’s Kevin Millar scored 3 runs to give Boston a 9-7 lead in the top of the eighth inning, 37-year-old reserve outfielder, Larry Walker, in the game as the DH, scored three runs of his own to put St. Louis back on top, 10-9. David Ortiz, who would have a difficult series, then failed to score in the ninth, handing St. Louis a 10-9 win and 2-1 series lead.

	1	2	3	4	5	6	7	8	9	F
Boston	4	0	0	0	0	0	2	3	0	9
St. Louis	4	0	0	1	1	1	0	3	X	10
W – Ray King (1-0)			L – Scott Williamson (0-1)				GW – Larry Walker (1)

Game 4: Jeff Suppan pitches St. Louis to 3-1 Series edge!

Jeff Suppan held the explosive Boston lineup to just 2 runs, leaving the game after the fifth inning with a narrow 3-2 lead. Relievers Dan Haren, Kiko Calero and Jason Isringhausen (who earned the save) combined to pitch 4 innings of shutout ball resulting in a 5-2 St. Louis win and 3-1 Series lead. As has become a pattern now for Boston, only Johnny Damon contributed on offense, providing Boston with an early 2-0 lead.

	1	2	3	4	5	6	7	8	9	F
Boston	0	2	0	0	0	0	0	0	0	2
St. Louis	0	0	0	3	0	0	2	0	X	5
W – Jeff Suppan (1-0)			L – Derek Lowe (0-1)				GW – Reggie Sanders (1)

Game 5: Boston holds on; Ramirez the star!

Manny Ramirez got things started for Boston, scoring 4 runs to begin the game. Albert Pujols countered with two runs in the bottom of first inning but that was as close as it got. Johnny Damon scored a run in the second and Jason Varitek added 3 more in the third to give Boston an 8-2 lead they maintained to the end.

	1	2	3	4	5	6	7	8	9	F
Boston	4	1	3	0	0	0	0	0	0	8
St. Louis	2	0	0	0	0	0	0	0	0	2
W – Bronson Arroyo (1-0)			L – John Carpenter (0-1)				GW – Manny Ramirez (1)

Game 6: The Curse lives on!

It seemed like everything was falling into place for Boston. Trailing 6-5 entering the bottom-half of the eighth inning, Kevin Millar scored 3 runs to give Boston an 8-6 lead. Closer Keith Foulke, who’d earned a save earlier in the Series and finished the regular season with 32 saves and a 2.17 ERA was set to face Jim Edmonds, a dangerous hitter who’d nonetheless failed to score in the 5 previous games.

Edmonds struck for 4 runs to give St. Louis a 10-8 lead.

Boston still had one more chance. David Ortiz was due up against St. Louis’ ace closer, Jason Isringhausen, who’d already saved two games in the series. Like Edmonds, the normally reliable Ortiz had failed to score in the Series. It appeared as though it might be deja vu all over again. Instead, Isringhausen extended Ortiz’s misery to oh-for-the-series and St. Louis escaped with a 10-8 win and 4-2 series victory. 

	1	2	3	4	5	6	7	8	9	F
St. Louis	0	4	0	0	0	0	2	0	4	10
Boston	0	3	2	0	0	0	0	3	0	8
W – Julian Tavarez (1-0)			L – Keith Foulke (0-1)				GW – Jim Edmonds (1)

Recap

This series really should have gone seven games. Boston’s 10-9 loss in game 3 and 10-8 loss in game 6 could have gone either way but both broke for St. Louis. Game 6 was particularly heartbreaking. In addition to Edmonds late outburst and Ortiz’s ninth inning failure, the normally reliable Jason Varitek committed a 2 run error in the 7th inning that, had he made the play, would have prevented Edmonds blast from being the game-winner.

The breaks seemed to find St. Louis in this series. It felt like Boston was … well… cursed!

As a longtime Seattle sports fan, I’m quite familiar with underachieving teams. If you look up the record for fewest yards in an NFL football game, for instance, you’ll find the Seattle Seahawks listed there.  Seattle concluded the 1979 season ranked fourth in the league in scoring but on November 4 they finished with an incredible -7 yards in offense in an embarrassing 24-0 loss to the Los Angeles Rams (on the plus side the game featured plenty of offense as the Rams accumulated 29 first downs, 303 yards rushing and 475 yards overall).

In pro basketball, the Seattle Super Sonics won 63 games in 1994 before becoming the first #1 seed to lose to a #8 seed in NBA playoffs history. It’s happened since but they were the first.

Then there were the 2001 Seattle Mariners. They tied the 1906 Chicago Cubs as the only team to win 116 games in a season and won their division by 14 games over a very good Oakland team.

The chinks in their armor started to appear in the Divisional Series against Cleveland. They were shutout 5-0 at home in game one, rebounded in game two then were routed in game three when the series moved to Cleveland. They fought back to win the series in five games.

When it came time to play the New York in the ALCS, they were on the verge of falling apart. They lost the first two games at home, managing to score just 4 runs on 10 hits. The series then moved to the Big Apple for the next three games.

Seattle shellacked the Yankees, 14-3, in the third game of the series then lost game four. In the decisive fifth game, they trailed 9-0 after six innings before eventually losing 12-3. Of the 11 teams with the most regular season wins in Major League Baseball history the Mariners are alone as the only team to not make it to the World Series.

The fourth team on that list, right behind the 1998 Yankees (who rolled through the postseason), are the 1954 Cleveland Indians, who won 111 games and lost just 43 (which works out to a higher winning percentage than Seattle). The Indians pitching staff featured four future Hall of Famers: Early Wynn, Bob Lemon, Bob Feller and reliever, Hal Newhouser. Toss in outfielder, Larry Doby, and they boasted five future Hall of Famers in all.

The New York Giants featured just three future Hall of Fame players, but one of them was named Willie Mays and it proved more than enough. The Giants swept the Indians in the World Series, outscoring them 21-9 over the four games.

A glutton for punishment, I decided I’d pit Seattle against Cleveland in a seven game series then, as was the case in an earlier match-up featuring 1927 New York against 1929 Philadelphia, I’d extend it to 20 games and see who came out on top.

Despite their lack of postseason success, both teams compiled impressive statistics. Led by Edgar Martinez, Ichiro Suzuki, Bret Boone, and John Olerud Seattle boasted a powerful offense that averaged 5.72 runs a game, best in the majors. Freddy Garcia (18-6, 3.05 ERA), Jamie Moyer (20-6, 3.43 ERA) and Aaron Sele (15-5, 3.60 ERA) anchored a strong starting rotation and Kazuhiro Sasaki (45 saves) and Arthur Rhodes (8-0, 1.72 ERA) were the leaders of a deep bullpen. Their combined 3.54 ERA led the majors.

Cleveland was also impressive, though perhaps a bit less so. Stars Larry Doby, Bobby Avila, Al Rosen and Al Smith led an offense that ranked fourth in runs scored at 4.78 per game. On the other hand, their pitching staff— the starters in particular— were uniformly brilliant, posting a 2.78 ERA that was far and away the best in baseball. Bob Lemon (23-7, 2.72 ERA) and Early Wynn (23-11, 2.73 ERA) were both 20-game winners and Mike Garcia (19-8, 2.64 ERA) fell just one win short of twenty while leading the starters in ERA.

Ray Narleski (13 saves, 2.22 ERA), Don Mossi (6-1, 1.94 ERA) and Hal Newhouser (7-2, 2.51 ERA) were magnificent in relief.

Series Outlook and Predictions

Despite their postseason blunders, these are two very good teams.

Seattle is better at the plate. While Cleveland’s offensive stars compare well to Seattle’s top players, Seattle is simply deeper. Cleveland can match the quartet of Ichiro Suzuki, John Olerud, Mike Cameron and Mark McLemore with Bobby Avila, Al Smith, Larry Doby and Vic Wertz. They can come close to offsetting Edgar Martinez with Al Rosen. But they have no answer for Bret Boone. The rest of Seattle’s lineup which, while not great, is nonetheless superior.

On the other hand, Cleveland holds the edge ON the mound. The problem for Cleveland is it’s a narrow edge, and not enough to overcome their disadvantage at the plate. My calculations suggest Seattle should score about 5.05 runs a game against the Cleveland pitching staff. I expect Cleveland to put up 4.79 runs a game against Seattle’s hurlers. Using these numbers, I give Seattle a 55% chance to win the series, most likely in 6 or 7 games.

Finally, I expect Seattle to win a small majority of the 20 game series.

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Game 1: Cleveland routs Seattle, 12-5!

Cleveland’s heavy hitters were mostly silent while lesser-known players George Strickland, Jim Hegan and Wally Westlake combined to score 9 runs. Freddy Garcia started for Seattle and allowed 5 runs before being replaced by Ryan Franklin, who was scorched for 7 runs in just two innings work.

	1	2	3	4	5	6	7	8	9	F
Seattle	0	0	0	2	0	0	0	2	1	5
Cleveland	0	0	0	3	0	2	4	3	X	12
W – Early Wynn (1-0)			L – Freddy Garcia (0-1)				GW – Jim Hegan (1)

Game 2: Edgar to the rescue!

Edgar Martinez scored two runs in the ninth inning to push Seattle past Cleveland, 8-6, after Seattle squandered an early 4-0 lead. Reliever Don Mossi was the loser for Cleveland.

	1	2	3	4	5	6	7	8	9	F
Seattle	4	0	0	0	2	0	0	0	2	8
Cleveland	1	0	3	2	0	0	0	0	0	6
W – Joel Pineiro (1-0)			L – Don Mossi (0-1)				GW – Edgar Martinez (1)

Game 3: Avila strikes crushing blow!

Bobby Avila scored four runs in the tenth inning to break a 1-1 tie and give Cleveland the win, 5-1. The Cleveland pitching staff was brilliant, holding Seattle scoreless until the ninth inning when Edgar Martinez scored a run to tie the game off ace closer, Ray Narleski, who was charged with a blown save. Bob Hooper pitched a scoreless tenth inning for Cleveland and earned the win.

	1	2	3	4	5	6	7	8	9	10	F
Cleveland	0	0	0	1	0	0	0	0	0	4	5
Seattle	0	0	0	0	0	0	0	0	1	0	1
W – Bob Hooper (1-0)			L – Jeff Nelson (0-1)				GW – Bobby Avila (1)

Game 4: Boone delivers!

Seattle second baseman Bob Boone scored 4 runs in the eighth inning to give Seattle a narrow 8-6 lead and Arthur Rhodes did the rest, pitching a scoreless ninth inning to earn his first save and preserve the victory. For the second game in a row, Bobby Avila was the best player on the field for Cleveland, only this time in defeat.

	1	2	3	4	5	6	7	8	9	F
Cleveland	4	0	0	1	0	0	0	1	0	6
Seattle	2	0	2	0	0	0	0	4	X	8
W – Norm Charlton (1-0)			L – Art Houtteman (0-1)				GW – Bret Boone (1)

Game 5: Early Wynn shuts out Seattle; Cleveland wins 1-0!

Early Wynn pitched a gem, shutting out the high-powered Seattle offense to lead Cleveland to a 1-0 win. Bobby Avila scored the only Cleveland run in the first inning and Wynn made it stick. Freddy Garcia was the tough-luck loser for Seattle.

	1	2	3	4	5	6	7	8	9	F
Seattle	0	0	0	0	0	0	0	0	0	0
Cleveland	1	0	0	0	0	0	0	0	X	1
W – Early Wynn (2-0)			L – Freddy Garcia (0-2)				GW – Bobby Avila (2)

Game 6: Cleveland whips Seattle, win series!

Cleveland posted double-digit runs for the second time in the series to claim a 10-6 win in a game that wasn’t nearly as close as the final score indicated. Cleveland led 7-0 after four innings and 10-0 entering the eight inning before a pair of 3-run shots by Boone and Martinez narrowed the score. The late outburst sullied starter Bob Lemon’s final pitching line but had no effect on the outcome. Lemon earned the win in the series clincher.

	1	2	3	4	5	6	7	8	9	F
Cleveland	0	3	0	4	0	0	3	0	0	10
Seattle	0	0	0	0	0	0	0	3	3	6
W – Bob Lemon (1-0)			L – Aaron Sele (0-1)				GW – Vic Wertz(1)

Recap

The results of the series seemed fitting. Seattle underachieved. The fact they lost was not all together surprising; it’s how they lost.

Seattle’s pitching staff surrendered 40 runs in the series, 6.7 a game. Light-hitting Jim Hegan accounted for 7 of those runs. Of Cleveland’s offensive stars, first baseman Vic Wertz (10 runs, 1 GW) and second baseman Bobby Avilla (10 runs, 2 GW) accounted for half the Cleveland runs. Meanwhile Larry Doby (3 runs) and Al Rosen (1 run) were content to play a smaller role.

Only Jamie Moyer, who pitched six shutout innings, shined for the Seattle pitching staff. Freddy Garcia (0-2, 4.15 ERA), Aaron Sele (0-1, 9.75 ERA) and Paul Abbott (0-0, 7.50 ERA) all disappointed.

Seattle performed better at the plate, scoring 28 run (4.7 a game) but many of those runs were meaningless. Martinez (7 runs) and Boone (9 runs), scored a total of 9 runs between them after the game had been decided.

Cleveland’s pitching starting staff performed well, particularly Wynn (2-0), who allowed a mere 2 runs in 14 innings, and Mike Garcia, who hurled 5 scoreless innings. Lemon (1-0) and Feller both posted high ERAs that were slightly misleading.

Fourteen games later…

It was bound to happen. After playing brilliantly in the first six games it was inevitable Cleveland would eventually fall apart. It didn’t happen right away.

After winning the 6-game series four game to two, Cleveland proceeded to win three of the next four by scores of 4-3, 7-4, 0-9 and 6-4, giving them a 7-3 record after 10 games. They then proceeded to lose 5 games in a row and 8 of the next 10.

The real problem was at the plate. After scoring 57 runs in the first 10 games, they scored only 30 in the next 10.

On the other hand, Seattle continued to score at roughly the same rate. John Olerud was quietly dazzling, Martinez and Boone flashed at various times, and Ichiro was steady. Pitching was the real difference. Seattle pitchers finished the 20-games with an 11-9 mark and 4.45 ERA. The Indians pitchers combined for a 4.91 ERA.

After dominating the first 10 games, in the end it was Cleveland who underachieved. Although their 9-11 mark against Seattle seems about right, their late collapse seemed eerily familiar.

Series Stats

1954 Cleveland Batting

Name	G	AB	RN	GW	R/G
Vic Wertz	20	20	16	1	.800
Bobby Avila	18	20	14	2	.700
Larry Doby	20	20	13	1	.650
Jim Hegan	18	18	12	2	.667
Al Smith	20	20	11	1	.550
Wally Westlake	20	18	9	1	.500
Al Rosen	20	16	8	1	.500
George Strickland	20	20	3	0	.150
Dave Philley	20	20	1	0	.050
Hal Naragon	2	2	0	0	.000
Hank Majeski	1	1	0	0	.000
Rudy Regaldo	1	1	0	0	.000
TOTALS	180	176	87	9	.494

1954 Cleveland Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Hal Newhouser	4	0	0	0	0	0	6	0	0	0.00
Ray Narleski	4	0	0	1	0	2	5	1	1	1.80
Mike Garcia	5	5	0	1	1	0	34	13	13	3.44
Bob Lemon	5	5	4	2	2	0	43	24	23	4.81
Early Wynn	5	5	4	2	3	0	40	22	22	4.95
Bob Hooper	3	0	0	1	0	0	5	3	3	5.40
Bob Feller	4	4	1	1	1	0	28	18	17	5.46
Don Mossi	4	0	0	0	2	0	4	3	3	6.75
Art Houtteman	4	1	0	1	2	1	11	14	14	11.45
TOTALS	38	20	9	9	11	3	176	98	96	4.91

2001 Seattle Batting

Name	G	AB	RN	GW	R/G
John Olerud	20	20	19	1	.950
Bret Boone	19	18	17	2	.944
Ichiro Suzuki	20	22	15	2	.682
Edgar Martinez	20	15	12	2	.800
Mark McLemore	19	19	10	0	.526
Mike Cameron	20	20	9	2	.450
Dan Wilson	20	20	6	2	.300
David Bell	20	20	6	0	.300
Carlos Guillen	20	20	4	0	.200
Stan Javier	1	1	0	0	.000
Ramon Vazquez	1	1	0	0	.000
TOTALS	180	176	98	11	.557

2001 Seattle Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Joel Pineiro	2	0	0	1	0	0	4	0	0	0.00
Arthur Rhodes	5	0	0	0	0	2	8	0	0	0.00
Brett Tomko	1	0	0	0	0	0	1	0	0	0.00
Jamie Moyer	5	5	0	2	0	0	33	4	4	1.09
Freddy Garcia	5	5	1	3	2	0	34	9	9	2.38
Norm Charlton	2	0	0	1	0	0	3	1	1	3.00
Jeff Nelson	4	0	0	1	1	0	9	5	5	5.00
Kazuhio Sasaki	6	0	0	1	1	3	9	4	4	4.00
Aaron Sele	5	5	1	1	3	0	34	20	20	5.29
Jose Paniagua	2	0	0	0	0	0	4	3	3	6.75
Paul Abbott	4	4	1	1	2	0	26	25	25	8.65
John Halama	3	1	0	0	0	0	6	6	6	9.00
Ryan Franklin	3	0	0	0	0	0	5	10	10	18.00
TOTALS	47	20	3	11	9	5	176	87	87	4.45

 

The 1979 World Series between Baltimore and Pittsburgh is one the earliest World Series I can remember. Pittsburgh and Baltimore played in a true Fall Classic that year, with Pittsburgh rallying from a 3-1 deficit to win in seven games.

I remember I bought my first tabletop baseball game that same year. It was Strat-O-Matic in the red box with the batter, catcher and umpire graphic that looked like it had been cut from pale green construction paper and appeared nothing like the major league ballplayers promised inside the box. Inside the box… I remember those first black and white basic cards. They came pre-cut in those days and already bundled into teams with narrow rubber bands. They were sturdier then, too, but I digress.

A lot of my interest in tabletop board games these days is nostalgia and, the truth is, I have a lot of nostalgia for those late seventies and early eighties seasons as a result. One of the first teams I created when I was rating teams for Baseball Trivia Challenge was the 1978 Seattle ball club (because that’s where I lived at the time) and the two teams I’m going to talk about here: 1979 Baltimore and Pittsburgh. After I’d finished the ratings for both teams a couple things sprang to mind.

To begin with, I wouldn’t characterize either team as great. Baltimore won 102 games and lost only 57, which is very good. Like the 1969 team, they played excellent defense. But outside Ken Singleton, Eddie Murray, Gary Roenicke and a few others, they weren’t a big-hitting team.

They scored 4.76 runs per game (R/G), sixth best in the majors just ahead of Pittsburgh, who ranked seventh at 4.75 runs per game. For context, California topped the majors at 5.35 R/G with Boston second at 5.26.

Baltimore’s starting pitching staff, which featured Cy Young Award Winner, Mike Flanagan, Dennis Martinez, Steve Stone, Scott McGregor and Jim Palmer were, with the exception of Stone (who was only so-so but would nonetheless win the Cy Young Award the following year), solid but not spectacular.

They had a solid bullpen but lacked the sort of shutdown closer we’re accustomed to today. That probably had more to do with the times than anything else. Tim Stoddard, Dave Ford, and Don Stanhouse all had excellent seasons and Sammy Stewart was a true long-reliever.

Pittsburgh had their own vibe— specifically, the Sister Sledge hit “We Are Family.” The patriarch of the family was 39-year-old, Willie “Pops” Stargell, who served as an inspiration to teammates both on and off the field. Named co-MVP in the National League, Stargell was quick to recognize his teammates contributions, dispensing “Stargell Stars” to his mates for great plays.

But while Stargell garnered the most attention, it was a man 11 years his junior who was Pittsburgh’s best player. Dave Parker entered the 1979 season fresh from winning the NL MVP the year prior and although 1979 was a regression year, it was still plenty spectacular. Indeed, the sturdy Parker managed to bang out 193 hits (6th best in the NL), bat .310 (7th best), and knock in 94 runs (8th best).  And, oh yeah, he also won a Gold Glove for his play in right field.

Pitching in hitter-friendly Three Rivers Stadium, the pitching staff performed admirably. Although none of the starting staff won more than John Candelaria’s 14 games, they performed well together as a group. The bullpen was a strength. Closer Kent Tekulve’s submarine delivery may have been a bit unusual but it achieved excellent results. Tekulve finished third in the majors with 31 saves and his 94 appearances topped all relievers. Enrique Romo, Grant Jackson and Jim Bibby were all solid contributors.

Series Outlook and Predictions

Statistically, the two teams appear evenly matched. Of course, such comparisons are often overly simplistic. Baltimore and Pittsburgh each scored almost exactly the same number of runs per game but Baltimore compiled those numbers with the help of a designated hitter. On the other hand, Pittsburgh played in a run-friendly park.

I was a math major in college so I did what all math majors do in these circumstances: I started scribbling out a few equations. Without getting into the details, I calculated that Pittsburgh should win roughly 55% of the time. This allowed me to determine they had about a 60% chance to win the series. I further determined the most likely outcome was for them to win in 6 or 7 games (6 games was most likely but by less than 1%).

Mind you these calculations were based on the Game 1 batting lineups and both managers, but especially Baltimore skipper, Earl Weaver, changed the batting lineups as the series progressed. Also, according to the ratings, none of these lineups featured Baltimore’s best batsmen in the lineup together. On the other hand, Chuck Tanner, Pittsburgh’s manager, seemed to do a better job selecting lineups conducive to scoring runs.

Finally, I tried to utilize the same starting lineups the teams used during the actual series. This extended to the pitching staff as well, though this was obviously influenced by individual game conditions. In the end, I think I did a fairly decent job.

And with that, “Play ball!”

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Game 1: Flanagan’s the man again!

Cy Young Award winning lefty, Mike Flanagan, was dominant, pitching 9 innings of shutout baseball to lead Baltimore to a 2-0 win and 1-0 lead in the series. Baltimore’s Eddie Murray scored the game-winning run in the first inning and shortstop, Mark Belanger, added an insurance run in the sixth. Pittsburgh starter, Bruce Kison, who had to leave the game after the fourth inning with an injury was the tough-luck loser.

	1	2	3	4	5	6	7	8	9	F
Pittsburgh	0	0	0	0	0	0	0	0	0	0
Baltimore	1	0	0	0	0	1	0	0	X	2
W – Mike Flanagan (1-0)			L – Bruce Kison (0-1)				GW – Eddie Murray (1)

Game 2: Foli wins it for Pittsburgh!

Baltimore pitcher, Jim Palmer, extended Pittsburgh’s scoreless inning steak to 14 before shortstop, Tim Foli,  struck for 3 runs in the sixth inning to put Pittsburgh ahead 3-0. It was all they needed and then some. All-Star Ken Singleton scored a run in the bottom of the ninth to avoid the shutout but it was far too little too late.

Bert Blyleven pitched 8 shutout innings for Pittsburgh and closer, Kent Tekulve, earned the save despite allowing the final run to Singleton.

	1	2	3	4	5	6	7	8	9	F
Pittsburgh	0	0	0	0	0	3	0	0	0	3
Baltimore	0	0	0	0	0	0	0	0	1	1
W – Bert Blyleven (1-0)			L – Jim Palmer (0-1)				GW – Tim Foli (1)

Game 3: Baltimore wins Game 3; Stewart, Bumbry shine

After starting pitcher, Scott McGregor, was hurt after the third inning, Baltimore’s Jack Dempsey knew he had to do something to help reliever, Sammy Stewart, who’d entered the game to replace him. Dempsey’s strategy— to argue balls and strikes with home plate umpire, Russ Goetz, and get tossed from the game— didn’t help in the least, but fortunately for Baltimore, Stewart barely noticed, pitching 5 solid innings in relief to earn the win and help give Baltimore a 2-1 Series edge.

Center fielder, Al Bumbry, was the hero at the plate, scoring 4 runs and giving Baltimore a lead they would never relinquish.

	1	2	3	4	5	6	7	8	9	F
Baltimore	0	0	0	4	1	0	0	1	0	6
Pittsburgh	1	0	0	0	0	2	0	0	0	3
W – Sammy Stewart (1-0)			L – John Candelaria (0-1)				GW – Al Bumbry (1)

Game 4: Heartbreak and Elation!

Dave Parker scored 4 runs in the bottom of the ninth inning to tie the game and NL MVP, Willie “Pops” Stargell, scored the winning run in the 11th inning to lead Pittsburgh to an improbable victory over Baltimore. The victory was critical for Pittsburgh as it prevented them from facing the unenviable prospect of being down three-games-to-one, instead tying the series at two games a piece.

For Baltimore the loss was devastating and would prove a harbinger of things to come.

	1	2	3	4	5	6	7	8	9	10	11	F
Baltimore	2	0	0	3	0	2	0	0	0	0	0	7
Pittsburgh	0	0	2	1	0	0	0	0	4	0	1	8
W – Enrique Romo (1-0)			L – Tippy Martinez (0-1)				GW – Willie Stargell (1)

Game 5: Slow Collapse!

For the second game in a row, Baltimore jumped out to a large early lead and couldn’t hold on, relinquishing the last of a 5-0 lead on Tim Foli’s 2 run outburst in the sixth inning then failing to mount a response. Mike Flanagan, who’d pitched brilliantly in his first outing, was decidedly less effective in this one, allowing 6 runs in six innings work before being replaced by Tim Stoddard.

Pittsburgh’s, Jim Rooker, started the game and allowed 5 runs through 5 innings before being replaced by fellow starter, Bert Byleven, in a surprise move by Pittsburgh skipper, Chuck Tanner. Blyleven was brilliant again, holding Baltimore scoreless over the last 4 innings.

	1	2	3	4	5	6	7	8	9	F
Baltimore	2	3	0	0	0	0	0	0	0	5
Pittsburgh	0	0	2	2	0	2	0	0	0	6
W – Bert Blyleven (2-0)			L – Mike Flanagan (1-1)				GW – Tim Foli (2)

Game 6: Onslaught!

Returning home for game 6 after two heartbreaking 1-run losses, Baltimore appeared flat out of the gate. Even so, after scoring on Bill Madlock’s error in the bottom of the sixth inning, Baltimore entered the seventh inning down just 2-1 with three-time Cy Young Award Winner, Jim Palmer, on the mound. It wasn’t the sort of scenario that augured disaster, but disaster struck all the same. Pittsburgh scored 7 runs in the final three frames— against three of Baltimore’s finest pitchers— turning a tight game into a blowout. The final score was a disheartening 9-1.

	1	2	3	4	5	6	7	8	9	F
Pittsburgh	0	2	0	0	0	0	2	2	3	9
Baltimore	0	0	0	0	0	1	0	0	0	1
W – John Candelaria (1-1)			L – Jim Palmer (0-2)				GW – Willie Stargell (2)

Recap

This one felt incomplete, like a book missing an important chapter. Just as in real-life, the statistics don’t help a lot. They paint a picture of a Baltimore team that struggled to score, tallying just 22 runs over the course of the series, but that’s a bit misleading. Coming into Game 6, Baltimore had outscored Pittsburgh 21-20 and had generally been the more explosive team.

It was true, of course, that some of Baltimore’s stars failed to produce as expected: Team MVP, Ken Singleton, and to a lesser extent left fielder, John Lowenstein (who was eventually replaced by Gary Roenicke) both come to mind. In addition, Palmer, Stoddard and Stanhouse all had a rough go of it in Baltimore’s Game 6 loss but they were otherwise solid. Palmer was a tough-luck loser in Game 2 (allowing just 3 runs in 9 innings), Stanhouse earned a save in Game 3 and Stoddard didn’t allow an earned run in two appearances.

The real difference was Baltimore’s inability to hold a lead. Going into game 4, Baltimore held a 2-1 series lead and entered the ninth inning ahead 7-3. After Singleton failed to ice the game with a run, Dave Parker smashed home 4 runs and the Pirates wound up winning 8-7 in 11 innings. Baltimore followed that disappointing effort by taking a 5-0 lead in game 5 before fading down the stretch again, losing 6-5.

Pittsburgh won in much the same fashion as they did in real-life, closing out the series with three straight wins. Their top three stars— Parker, Stargell and Madlock— all played well as did future Hall-Of-Famer, Bert Blyleven, who pitcher 12 scoreless innings, including 4 in relief.

After going scoreless through the first 14 innings of the series, Pittsburgh’s offense responded with 29 runs over the last 41 (a clip equivalent to 6.4 runs per game).

In an evenly-matched series, they emerged the best!

Series Stats

1979 Baltimore Batting

Name	G	AB	RN	GW	AVG
Al Bumbry	5	5	7	1	1.400
Eddie Murray	6	7	5	1	.714
Kiko Garcia	4	4	3	0	.750
Gary Roenicke	2	2	3	0	1.500
pitcher	6	6	1	0	.167
Mark Belanger	2	2	1	0	.500
Doug DeCinces	6	6	1	1	.167
Ken Singleton	6	5	1	1	.200
John Lowenstein	4	5	0	0	.000
Billy Smith	4	4	0	0	.000
Rick Dempsey	5	5	0	0	.000
Dave Skaggs	1	1	0	0	.000
Rich Dauer	2	2	0	0	.000
Benny Ayala	1	1	0	0	.000
TOTALS	54	55	22	4	.400

1979 Baltimore Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Tim Stoddard	2	0	0	0	0	0	3	2	0	0.00
Scott McGregor	1	1	0	0	0	0	3	1	1	3.00
Mike Flanagan	2	2	1	1	1	0	15	6	6	3.60
Sammy Stewart	1	0	0	1	0	0	5	2	2	3.60
Jim Palmer	2	2	1	0	2	0	16	7	7	3.94
Tippy Martinez	1	0	0	0	1	0	2	1	1	4.50
Dennis Martinez	1	1	0	0	0	0	9	7	6	6.00
Don Stanhouse	2	0	0	0	0	1	2	3	3	13.50
TOTALS	12	6	2	2	4	1	55	29	26	4.25

1979 Pittsburgh Batting

Name	G	AB	RN	GW	AVG
Tim Foli	6	6	7	2	1.167
Dave Parker	6	5	7	0	1.400
Bill Madlock	6	6	4	0	.667
Willie Stargell	6	7	3	2	.429
Phil Garner	6	6	3	0	.500
Ed Ott	1	1	2	0	2.000
John Milner	1	1	2	0	2.000
Omar Moreno	6	7	1	0	.143
pitcher	6	6	0	0	.000
Steve Nicosia	5	5	0	0	.000
Bill Robinson	5	5	0	0	.000
TOTALS	54	55	29	4	.527

1979 Pittsburgh Pitching

Name	G	GS	CG	W	L	Sv	IP	RN	ER	ERA
Bert Blyleven	2	1	0	2	0	0	12	0	0	0.00
Grant Jackson	2	0	0	0	0	0	3	2	0	0.00
Don Robinson	1	0	0	0	0	0	1	0	0	0.00
Enrique Romo	1	0	0	1	0	0	4	0	0	0.00
Bruce Kison	2	1	0	0	1	0	6	2	1	1.50
John Candelaria	2	2	1	1	1	0	14	6	6	3.86
Jim Rooker	2	1	0	0	0	0	9	6	6	6.00
Kent Tekulve	1	0	0	0	0	1	1	1	1	9.00
Jim Bibby	1	1	0	0	0	0	5	5	5	9.00
TOTALS	14	6	1	4	2	1	55	22	19	3.11