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Your projection says what? What an Excel spreadsheet, Bill James, and last year's teams might say about 2024 expectations in the AL East


Greetings everyone,

We've reached that part of the offseason where spring training is just around the corner and the various outlets are starting to put out their projections and predictions on the upcoming baseball season. By now we've seen what PECOTA and Fangraphs have to say, and there's always our friends in the desert to gage what the consensus is for the upcoming season. Most of the ones we've seen come out tend to have the same theme- the Yankees are the favorite with the Orioles and Rays taking a significant step back. I had the same reaction as most of you- namely, how in the world?

To try to answer this, I recalled the Baseball Book 1992, in which Bill James uses seven criteria to determine how a given team will perform the following season, which he labeled Leading Indicators. The seven are AAA team performance, team age, Pythagorean record, the plexiglass principle, competitive balance, the ratio to team runs scored to team RBI, and W-L record over the second half of the season. Since that book came out, the BABIP effects have been studied, so those will also be included in this study. Finally, the effects of player moment via free agency and trades must be accounted for to gain the best guess as to where a team's talent level is heading into 2024.

In doing so, I relied on my annual 1000-year simulation I developed over 10 years ago to look at what a team's true level was. Based on an Excel spreadsheet and a bunch of VB scripts, I use over 100 different data categories that separate events based on the ballpark, team, and individual stats. These stats were the basis of the simulation.

Simulation details

The simulation itself runs like an actual game. It goes as follows:

- Before each PA, determine if there was a SB, CS, pickoff, error, balk, wild pitch or passed ball. All these odds are based off the stats of the pitcher and baserunner.

- Determine odds of 15 possible outcomes of a PA based on platoon splits, park factors, and of course the pitcher and batter

- Based on the outcome of the PA, and the stats of the defense, determine if there was an error on the play, and place all baserunners. Placement is based on the team defense in the field, and individual baserunning of the baserunners involved. If there was an insufficient sample size (<25 instances) a weighted average of team stats and individual stats was used.

- Determine the pitcher for the next PA, based on pitch count, game situation and game performance

- Determine the batter for the next PA

Stats for the simulation, including wins and losses, are recorded and then are output for the iteration. For a 2,430 game season, it takes my computer roughly 100 seconds to do.

Simulation results

Anyway, back to the list of Leading Indicators. I used this spreadsheet to account for Pythagorean record, the ratio to runs scored to team RBI, the Plexiglass Principle and the competitive balance indicators. I did this because the first three are luck-driven, whereas I felt competitive balance was too- in that teams with winning records will tend to be more lucky than not. Running 1000 simulations, where luck (hopefully) is boiled out of the equation and all the breaks even out, will serve as our baseline for where a team's talent level was in 2023.

Here are the results of the simulation for the AL East in 2023:

Wild cards Division playoffs average min max real
BAL 592.33 184.33 776.67 91.90 67 113 101
TBA 195.33 780.83 976.17 99.58 79 119 99
TOR 291.23 28.33 319.57 84.49 64 104 89
NYA 35.23 2.00 37.23 76.44 56 97 82
BOS 42.83 4.50 47.33 77.18 58 97 78

Did the Orioles get lucky last year? This is saying they were, they outperformed the simulation by 9 wins, which was enough to "steal" the division from Tampa. They were, however, a worthy playoff team at least which couldn't be said for some of the other teams that made it (looking at you, Miami) or Boston or New York. Toronto, while a bit lucky themselves to have made it based on this, at least acquitted themselves as a legit possibility even if not a probability.

Next, let's look at the other indicators.

Team Age

The average player age in MLB was just under 28.5 this year, which broke down to 28 for batters and 29 for pitchers. The younger your team was, the more you stand to improve and vice versa. Since I don't know the scale of these indicators, I had to rely on my gut which I will admit adds uncertainty to this whole exercise, but an educated guess is better than no guess. I came up with each year away from the average was worth 2 games- being younger by a year means two more wins, being older, two less wins. Here's how the AL East shook out:

BAL 1.87
TBA 2.47
TOR -3.68
NYA -0.98
BOS -2.48

What stands out to me here is that while the Orioles were young, the Rays were younger, and in fact were the youngest team to make the postseason last year- the Orioles were second youngest. What this means is thinking that the Orioles will stay on top of this division simply because of their age is misguided- Tampa is the young team in the division. Meanwhile, Toronto was the oldest team in the AL, and Boston and New York are no spring chickens either.

2nd half performance

What I did here was compare every team's post-All-star break record to their overall record. I didn't weigh this one as heavily since it can be more influenced by luck and smaller sample sizes, but included it nonetheless. If your record was better post-break you got some more wins, a worse record took some away.

BAL 1.02
TBA -0.85
TOR 0.00
NYA -2.07
BOS -2.95

Not much to see here, except Tampa not quite being able to maintain its torrid start, the Orioles slowly picking up steam, and Boston and New York falling apart down the stretch. Nothing really new here, but now we have numbers to quantify it.

AAA team

Teams with a better farm team are better able to withstand injuries and are capable of bringing up talented players that aren't accounted for in the other methods. For every 20 percentage points a AAA team was above .500 in 2023, one win was added to that team's MLB total for 2024- and vice versa.

BAL 5.20
TBA 4.35
TOR 0.70
NYA -0.35
BOS 1.85

First thing is the division appears to have some good, if not great, talent as a whole at the AAA level. Only New York had a sub-.500 team, and not by much. The Orioles, as we all know, have some elite talent here, but the Rays appear to have talent to pull from here as well. Again, this points to not being dismissive of Tampa.

BABIP

BABIP has proven to be mostly luck driven. A team that hits for a high BABIP should expect a downturn, whereas a low BABIP team can expect an uptick in their offense. Same thing with their pitching- a high BABIP allowed may indicate better pitching performances ahead, and a low BABIP worse. Take the differences, up or down from league average, which for 2023 was .297, add them together, and for every five points up or down I added or took away a win.

BAL -2.20
TBA -4.60
TOR -1.00
NYA 3.00
BOS -0.60

This one I'm a little hesitant to even use in the first place since this correlated pretty strongly with a team's record. I decided to leave it in because it acts as something of a weight- that is, yes, bad teams can expect to pick up some wins here and good ones to lose them, but the relative distance is important too. In other words the difference in BABIP between two good teams- while they'll likely both indicate they got lucky- may still hold significance if one team's BABIP ratio is really out of line with the other. That being said, the Orioles did OK for a team in their echelon, and perhaps the BABIP fairy wasn't kind to the Yankees last year all things considered.

Offseason

Based on player movement so far, take wins away from teams that have lost free agents and give them to teams that signed them, using bWAR. Likewise, swap player WAR for any trades. Add them up to get the final indicator for this study.

BAL 0.9
TBA -1.3
TOR 4.7
NYA 4.7
BOS -4.8

If you think the O's total here should be higher based on the Burns trade and to a lesser extent the Kimbrel signing, bear in mind they did lose a couple of 1-2 WAR players in Gibson and Frazier. If you think the Yankees total should be higher based on the Soto trade, bear in mind they sent a chunk of their very good bullpen from last year to San Diego. They've improved their team no doubt, but the magnitude of it I feel has been greatly exaggerated. Boston continues to be enigmatic, trading away pieces while maintaining they're trying to be competitive.

All right, enough talking, let's see how this all adds up:

Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
BAL 91.90 1.87 1.02 5.20 -2.20 0.93 98.72
TBA 99.58 2.47 -0.85 4.35 -4.60 -1.30 99.66
TOR 84.49 -3.68 0.00 0.70 -1.00 4.73 85.24
NYA 76.44 -0.98 -2.07 -0.35 3.00 4.73 80.77
BOS 77.18 -2.48 -2.95 1.85 -0.60 -4.80 68.20

And in the end, I just don't get it. I don't get how any major media outlet, sportsbook, advanced projection, etc, can have the Yankees ahead of the Orioles or Rays. Did the Orioles overachieve last year? Yes, but they have plenty of factors working in their favor that the Yankees don't, factors which point to a better season. The only thing I can think of, and it's a fair one, is that the Yankees lost so many games to injury that it cost them the division. I just don't see it. Can we add some games to their total assuming they stay healthy? Of course, but that's a BIG if and it still doesn't come close to bridging the gap, even after accounting for their relatively good offseason. Want me to add 8 games for Rodon and Judge being healthy? OK, they're at 89 now- and we're assuming the rest of their aging stars stay productive. Want me to knock off 3 for Bautista being hurt? Seems fair to me, that lowers the O's 96 wins. Still not enough. Think some of the multiples I assigned to the above indicators are out of whack? OK, but the Orioles and Rays are ahead of the Yankees in just about every category I guessed at- the offseason is a straight-up WAR calculation and doesn't have any fudge factors associated with it. We won't even talk about what Rays fans are thinking, since most outlets I've seen have them not just behind New York and Baltimore but Toronto too.

My math says it doesn't add up. Given everything above I'd make Tampa the slight favorite, then Baltimore, with New York indeed getting some extra wins due to health to finish a somewhat distant third, just ahead of Toronto. The media does seem to have Boston in its rightful place.

I'll leave you with the expected standings for all MLB on my way out, in case you were curious about that. Plenty of stuff to digest here, but this is long enough and I'll leave it to you in the comments if you want to discuss it more. If you've made it this far, you have my thanks.

Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
TBA 99.58 2.47 -0.85 4.35 -4.60 -1.30 99.66
BAL 91.90 1.87 1.02 5.20 -2.20 0.93 98.72
TOR 84.49 -3.68 0.00 0.70 -1.00 4.73 85.24
NYA 76.44 -0.98 -2.07 -0.35 3.00 4.73 80.77
BOS 77.18 -2.48 -2.95 1.85 -0.60 -4.80 68.20
Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
MIN 96.59 -0.98 2.73 3.40 -2.20 -7.97 91.57
CLE 86.00 6.37 -1.93 -1.85 -0.20 -4.63 83.75
DET 76.56 2.77 2.64 -1.35 -1.00 1.47 81.08
KCA 62.05 2.32 3.84 -1.55 1.60 6.47 74.72
CHA 60.36 -0.08 -2.40 -7.20 2.20 3.60 56.48
Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
TEX 94.08 -2.63 -1.02 4.85 -4.20 0.23 91.31
SEA 92.64 2.62 2.29 0.65 -2.60 -5.93 89.67
HOU 91.00 -1.58 0.39 -4.65 -0.60 0.13 84.69
ANA 76.10 0.52 -2.81 -1.50 1.60 -8.37 65.54
OAK 48.81 2.77 2.43 0.15 6.40 0.00 60.56
Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
ATL 103.66 -1.28 -1.96 -1.35 -0.60 3.30 101.77
PHI 99.91 -1.88 0.99 2.40 -3.40 -3.10 94.92
NYN 78.33 -5.78 -0.23 -4.10 4.60 3.77 76.59
WAS 58.89 3.22 2.39 -2.40 2.60 1.80 66.50
FLO 72.70 -0.08 -3.78 -1.50 -1.20 -4.53 61.60
Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
CHN 91.68 -1.58 2.46 2.90 -2.20 -3.63 89.63
SLN 73.05 -0.38 1.00 -1.15 6.00 7.07 85.59
MIL 88.54 -0.08 1.89 3.05 -4.60 -3.80 85.00
CIN 80.48 3.67 -2.46 0.35 -0.80 0.70 81.93
PIT 69.88 3.07 0.85 -1.35 1.00 1.00 74.44
Spreadsheet Age 2nd half AAA BABIP Offseason Ex. Wins
LAN 99.20 -3.08 2.70 5.40 -3.00 11.90 113.11
ARI 80.83 1.57 -3.39 4.35 0.80 4.33 88.49
SDN 88.58 -3.23 1.77 -4.35 1.00 -7.87 75.90
SFN 77.03 -2.18 -3.55 -2.50 3.00 1.00 72.80
COL 53.49 -1.28 -0.60 -2.35 1.20 -1.20 49.25

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