2020 Off-Season Free Agent Signing AI Projections: Position Players

Top free agent Anthony Rendon - Photo by Elsa/Getty Images

Predicting Big $ Contracts

The 2020 off-season and free agency period in MLB has begun. While we probably won’t have multiple $300M+ contracts like last year’s Harper-Machado-Trout bonanza; we do have several interesting stories to follow with Anthony Rendon, Stephen Strasburg, and Gerrit Cole; all coming off World Series heroics.

As an exercise in curiosity and perhaps a public service to the financial planners for this year’s free agents*, I wanted to develop an analytical method for predicting free agency outcomes: who will sign for how much and for how long and with whom?

*I’m talking about the financial planners for guys like Robinson Chirinos and other; not Gerrit Cole’s financial planner. That guy has it easy.

Methodology

To do this, I took advanced analytics season results for all players from Fangraphs going back several seasons, free-agent signings from Cot’s Baseball Contracts, and AI modeling tools from DataRobot to develop predictions for this year’s free-agent class. Effectively, the AI models I built looked at several years of player performance, then compared that against free-agent contracts signed subsequent to performance to learn about relationships between player performance and other factors with free-agent value.

The other factors I included in the predictions were relative value and macro market conditions. In all, there were three categories of variables that I used to predict contracts:

  1. Absolute Player Performance: how the player performed in a vacuum (most stats and analytics we attribute to each player – a proxy for asset value)
  2. Relative Player Performance: How the player performed relative to others at his position (this is meant to be a crude proxy for market supply in this supply-demand market).
  3. Market Conditions: How much teams are spending in this offseason, with committed spend and spending trends as proxies (this is the market demand proxy)

What this is NOT: A Forecast of Value

These models were built to predict market transaction prices, NOT player value. Value will inevitably be created and destroyed by players that over- or under-perform their expectations, but that’s not what this model is looking for.

Position Player Contracts & Future Posts

This post will just look at expected contract terms (years and dollars) for position players. Future posts will look at pitchers, as well as predictions on destinations and how that could affect final contract value.

I will also continue to update these predictions, as each signing in this off-season will offer valuable information to help inform predictions for other players, so stay tuned!

2020 Position Player Contract Predictions

PlayerAverage Annual ValueYearsTotal Contract Value
Anthony Rendon $               21,063,964 7 $          147,447,750
Josh Donaldson $               18,105,112 6 $          108,630,669
Nicholas Castellanos $               15,550,854 3 $            46,652,562
Marcell Ozuna $               21,155,203 2 $            42,310,406
Jose Abreu $               10,275,342 3 $            30,826,025
Howie Kendrick $               10,112,487 2 $            20,224,974
Brett Gardner $               10,000,634 2 $            20,001,269
Kole Calhoun $                 9,865,025 2 $            19,730,050
Yasiel Puig $               18,321,945 1 $            18,321,945
Eric Thames $                 8,854,942 2 $            17,709,884
Corey Dickerson $                 7,785,726 2 $            15,571,452
Avisail Garcia $               15,368,255 1 $            15,368,255
Asdrubal Cabrera $                 6,831,499 2 $            13,662,997
Jonathan Schoop $                 6,741,736 2 $            13,483,472
Brian Dozier $               12,386,253 1 $            12,386,253
Justin Smoak $               11,724,017 1 $            11,724,017
Robinson Chirinos $                 5,724,032 2 $            11,448,064
Eric Sogard $                 5,536,218 2 $            11,072,435
Didi Gregorius $               10,937,351 1 $            10,937,351
Lonnie Chisenhall $                 9,974,120 1 $              9,974,120
Todd Frazier $                 9,833,767 1 $              9,833,767
Travis d’Arnaud $                 8,982,666 1 $              8,982,666
Mitch Moreland $                 8,781,348 1 $              8,781,348
Jose Iglesias $                 4,284,485 2 $              8,568,969
Logan Morrison $                 7,870,225 1 $              7,870,225
Brock Holt $                 7,725,734 1 $              7,725,734
Brad Miller $                 7,450,786 1 $              7,450,786
Steve Pearce $                 7,439,719 1 $              7,439,719
Gordon Beckham $                 3,707,617 2 $              7,415,233
Matt Joyce $                 7,145,916 1 $              7,145,916
Hunter Pence $                 7,090,897 1 $              7,090,897
Francisco Cervelli $                 6,884,713 1 $              6,884,713
Neil Walker $                 6,380,041 1 $              6,380,041
Cameron Maybin $                 6,333,086 1 $              6,333,086
Jason Castro $                 5,973,642 1 $              5,973,642
Yonder Alonso $                 5,745,456 1 $              5,745,456
Stephen Vogt $                 5,627,733 1 $              5,627,733
Rajai Davis $                 5,038,365 1 $              5,038,365
Alex Avila $                 5,023,928 1 $              5,023,928
Rene Rivera $                 5,009,883 1 $              5,009,883
Curtis Granderson $                 5,003,374 1 $              5,003,374
Gerardo Parra $                 4,993,965 1 $              4,993,965
Adam Jones $                 4,897,382 1 $              4,897,382
Martin Maldonado $                 4,508,043 1 $              4,508,043
Ben Zobrist $                 4,431,368 1 $              4,431,368
Melky Cabrera $                 4,309,284 1 $              4,309,284
Mark Trumbo $                 4,303,055 1 $              4,303,055
Russell Martin $                 3,946,136 1 $              3,946,136
Logan Forsythe $                 3,787,685 1 $              3,787,685
Jordy Mercer $                 3,765,403 1 $              3,765,403
Adeiny Hechavarria $                 3,566,447 1 $              3,566,447
Jarrod Dyson $                 3,375,988 1 $              3,375,988
Austin Romine $                 3,071,744 1 $              3,071,744
Matt Wieters $                 2,997,167 1 $              2,997,167
Jon Jay $                 2,768,360 1 $              2,768,360
Ryan Flaherty $                 2,549,306 1 $              2,549,306
Jonathan Lucroy $                 2,539,387 1 $              2,539,387
Sean Rodriguez $                 2,140,430 1 $              2,140,430
Martin Prado $                 2,100,235 1 $              2,100,235
Drew Butera $                 1,972,815 1 $              1,972,815

Last update: November 5, 2019

Help me understand how you calculated this:

As noted above, I combined multiple sources of data, and performed some additional analysis to create better signals in the data for true player market value. I then ran this data through DataRobot, an Auto-ML platform to help me construct the optimal AI models for prediction. The resulting Total Contract Value model looked to the following variables for greatest prediction value:

Stay tuned as we also calculate pitcher contracts, including the massive (and risky) contracts coming to Gerrit Cole and Stephen Strasburg.

What Drove The Predictions

First, age was a major factor in predicting Total Contract Value. The chart below shows, as would be expected, older free agents get smaller contracts.

Next, Prior 3-Years WAR proved to be very important to predictions, which makes sense – players who produced more over a sustained period of time should earn bigger contracts.

This 3-Years Prior WAR metric also reveals an expected but important truth about the model: it does NOT perform well at the upper-extremes. In the chart below, the orange marks are actual contract values from the past, and blue values are what the model would’ve predicted. As you can see, the actual values start exceeding the predicted values by significant amounts for most players above 10 3-Years Prior WAR. For contracts like the 2019 paydays, the model would’ve performed poorly.

This could mean that Bryce Harper and Manny Machado got over-paid significantly due to an irrational market, or it could mean there is something else going on with these players that the model doesn’t account for, such as marketing and revenue benefits from adding these marquee players that justify the additional cost.

Lastly, if a player rejected a qualifying offer, the model attributes significantly more value to the player. This is intuitive and obvious to a person, but is very important context to the AI.

These variables are combined with many more to drive the AI models, with the best models I eventually relied upon being those that made the most sense of the 80+ variables available to them to predict contracts.

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2 replies to “2020 Off-Season Free Agent Signing AI Projections: Position Players”

  1. […] I laid out my approach to using AI to predict 2020 off-season contracts and predicted position player terms. The results […]

  2. […] This post consolidates my findings from position player predictions and pitcher predictions at http://www.baseball-pop.com into a single list of top 2020 Free Agents and […]

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