Which teams have over or under-performed their expectations over the last 5 years?

Posted: March 7, 2014 in Managers, Organizational Issues, Projections

Ok, enough of the bad Posnanski and Woody Allen rants and back to some interesting baseball analysis – sort of. I’m not exactly sure what to make of this, but I think you might find it interesting, especially if you are a fan of a particular team, which I’m pretty sure most of you are.

I went back five years and compared every team’s performance in each and every game to what would be expected based on their lineup that day, their starting pitcher, an estimate of their reliever and pinch hitter usage for that game, as well as the same for their opponent. Basically, I created a win/loss model for every game over the last five years. I didn’t simulate the game as I have done in the past. Instead, I used a theoretical model to estimate mean runs scored for each team, given a real-time projection for all of the relevant players, as well as the run-scoring environment, based on the year, league, and ambient conditions, like the weather and park (among other things).

When I say “real-time” projections, they are actually not up-to-the game projections. They are running projections for the year, updated once per month. So, for the first month of every season, I am using pre-season projections, then for the second month, I am using pre-season projections updated to include the first month’s performance, etc.

For a “sanity check” I am also keeping track of a consensus expectation for each game, as reflected by the Las Vegas line, the closing line at Pinnacle Sports Book, one of the largest and most respected online sports books in the internet betosphere.

The results I will present are the combined numbers for all five years, 2009 to 2013. Basically, you will see something like, “The Royals had an expected 5-year winning% of .487 and this is how they actually performed – .457.” I will present two expected WP actually – one from my models and one from the Vegas line. They should be very similar. What is interesting of course is the amount that the actual WP varies from the expected WP for each team. You can make of those variations what you want. They could be due to random chance, bad expectations for whatever reasons, or poor execution by the teams for whatever reasons.

Keep in mind that the composite expectations for the entire 5-year period are based on the expectation of each and every game. And because those expectation are updated every 6 months by my model and presumably every day by the Vegas model, they reflect the changing expected talent of the team as the season progresses. By that, I mean this: Rather than using a pre-season projection for every player and then applying that to the personnel used or presumed used (in the case of the relievers and pinch hitters) in every game that season, after the first 30 games, for example, those projections are updated and thus reflect to some extent, actual performance that season. For example, last year, pre-season, Roy Halladay might have been expected to have a 3.20 ERA or something like that. After he pitched horribly for a few weeks or months, and it was well-known that he was injured, his expected performance presumably changed in my model as well as in the Vegas model. Again, the Vegas model likely changes every day, whereas my model can only change after each month, or 5 times per season.

Here are the combined results for all five years (NL 2009-2013):

Team

My Model

Vegas

Actual

My Exp. Starting Pitching (RA9-)

Actual Starting Pitching (FIP-)

My Exp. Batting (marginal rpg)

Actual Batting (marginal rpg)

ARI

.496

.495

.486

103

103

0

-.08

ATL

.530

.545

.564

100

97

.25

.21

CHC

.488

.478

.446

103

102

-.09

-17

CIN

.522

.517

.536

104

108

.01

.12

COL

.494

.500

.486

102

96

-.04

-.09

MIA

.493

.472

.453

102

102

.01

-.05

LAD

.524

.526

.542

96

99

.02

-.03

MLW

.519

.509

.504

105

108

.13

.30

NYM

.474

.470

.464

106

108

-.02

.01

PHI

.516

.546

.554

96

98

-.01

.07

PIT

.461

.454

.450

109

111

-.19

-.28

SDP

.469

.463

.483

110

115

-.12

-.26

STL

.532

.554

.558

100

98

.23

.40

SFG

.506

.518

.515

98

102

-.19

-.30

WAS

.497

.484

.486

103

103

.01

.07

If you are an American league fan, you’ll have to wait until Part II. This is a lot of work, guys!

By the way, if you think that the Vegas line is remarkably good, and much better than mine, it is at least partly an illusion. They get to “cheat,” and to some extent they do. I can do the same thing, but I don’t. I am not looking at the expected WP and result of each game and then doing some kind of RMS error to test the accuracy of my model and the Vegas “model” on a game-by-game basis. I am comparing the composite results of each model to the composite W/L results of each team, for the entire 5 years. That probably makes little sense, so here is an example which should explain what I mean by the oddsmakers being able to “cheat,” thus making their composite odds close to the actual odds for the entire 5-year period.

Let’s say that before the season starts Vegas thinks that the Nationals are a .430 team. And let’s say that after 3 months, they were a .550 team. Now, Vegas by all rights should have them as something like a .470 team for the rest of the season – numbers for illustration purposes only – and my model should too, assuming that I started off with .430 as well. And let’s say that the updated expected WP of .470 were perfect and that they went .470 for the second half. Vegas and I would have a composite expected WP of .450 for the season, .430 for the first half and .470 for the second half. The Nationals record would be .510 for the season, and both of our models would look pretty bad.

However, Vegas, to some extent uses a team’s W/L record to-date to set the lines, since that’s what the public does and since Vegas assumes that a team’s W/L record, even over a relatively short period of time, is somewhat indicative of their true talent, which it is of course. After the Nats go .550 for the first half, Vegas can set the second-half odds as .500 rather than .470, even if they think that .470 is truly the best estimate of their performance going forward.

One they do that, their composite expected WP for the season will be (.430 + .500) / 2, or .465, rather than my .450. And even if the .470 were correct, and the Nationals go .470 for the second half, whose composite model is going to look better at the end of the season? Theirs will of course.

If Vegas wanted to look even better for the season, they can set the second half lines to .550, on the average. Even if that is completely wrong, and the team goes .470 over the second half, Vegas will look even better at the end of the season! They will be .490 for the season, I will be .450, and the Nats will have a final W/L percentage of .490! Vegas will look perfect and I will look bad, even though we had the same “wrong” expectation for the first half of the season, and I was right on the money for the second half and they were completely and deliberately wrong. Quite the paradox, huh? So take those Vegas lines with a grain of salt as you compare them to my model and to the final composite records of the teams. I’m not saying that my model is necessarily better than the Vegas model, only that in order to fairly compare them, you would have to take them one game at a time, or always look at each team’s prospective results compared to the Vegas line or my model.

Here is the same table as above, ordered by the difference between my expected w/l percentage and each team’s actual w/l percentage. The firth column is that difference. Call those differences whatever you want – luck, team “efficiency,” good or bad managing, player development, team chemistry, etc. I hope you find these numbers as interesting as I do!

Combined results for all five years (NL 2009-2013), in order of the “best” teams to the “worst:”

Team

My Model

Vegas

Actual

Difference

My Exp. Starting Pitching (RA9-)

Actual Starting Pitching (FIP-)

My Exp. Batting (marginal rpg)

Actual Batting (marginal rpg)

PHI

.516

.546

.554

.038

96

98

-.01

.07

ATL

.530

.545

.564

.034

100

97

.25

.21

STL

.532

.554

.558

.026

100

98

.23

.40

LAD

.524

.526

.542

.018

96

99

.02

-.03

SDP

.469

.463

.483

.014

110

115

-.12

-.26

CIN

.522

.517

.536

.014

104

108

.01

.12

SFG

.506

.518

.515

.009

98

102

-.19

-.30

COL

.494

.500

.486

-.008

102

96

-.04

-.09

NYM

.474

.470

.464

-.010

106

108

-.02

.01

PIT

.461

.454

.450

-.010

109

111

-.19

-.28

ARI

.496

.495

.486

-.010

103

103

0

-.08

WAS

.497

.484

.486

-.011

103

103

.01

.07

MLW

.519

.509

.504

-.015

105

108

.13

.30

MIA

.493

.472

.453

-.040

102

102

.01

-.05

CHC

.488

.478

.446

-.042

103

102

-.09

-.17

As you can see from either chart, it appears as if my model over-regresses both batting and starting pitching, especially the former.

Also, a quick and random observation from the above chart – it may mean absolutely nothing. It seems as though those top teams, most of them at least, have had notable, long-term, “players’ managers,” like Manuel, LaRussa, Mattingly, Torre, Black, Bochy, and Baker, while you might not be able to even recall or name most of the managers of the teams at the bottom. It will be interesting to see if the American League teams evince a similar pattern.

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Comments
  1. Tim Boyer says:

    Great work! Given your model vs the Vegas “cheating model” I think it’s actually remarkable how close your model is.

    Can’t wait for the AL results.

    • MGL says:

      Thanks Tim. I am looking forward to seeing the AL results as well. It is difficult to evaluate the Vegas lines because of the “cheating” effect without looking at their lines on a game-by-game basis and seeing how the teams and pitchers do prospectively.

      That’s not to say that the oddsmakers deliberately “cheat” in order to make their lines look good in the aggregate at the end of the season. They have no reason to do that. In fact, if they did, they would get killed by the bettors, so they surely don’t do that.

      It is just that they put a lot of (too much) weight onto a team’s prior results when making a line each day, partially because the public does, and partially because they don’t have the time, inclination, or know-how to do a detailed, updated projection on each player every day of the season.

  2. MGL says:

    To be clear, what the over-performing teams, compared to my model, have in common is that they are mostly above average teams, and the under-performing ones are below average, according to MY PROJECTIONS. Theoretically, there should be no correlation. The reason there is, is because of my too-aggressive regressions for pitchers and batters.

  3. Xeifrank says:

    I don’t think Vegas cheats. I think their model and yes it is a model is more sensitive to changes in player talent. Your model likely over-regresses as most player projection systems do. Thanks for the interesting post. 🙂

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