Counting Stat Estimator For Hitters On The Move

In leagues with Runs and RBIs as categories, predicting how a player’s mix will change with a new team can be guessing. Some clown at Fantrax yesterday wrote the following:

“As for Headley, his value drops by going to a worse offensive team in a pitcher-friendly park. Part of the decline could be offset by a move up in the lineup since he mainly batted seventh for the Yankees last season.”

It could go up, it could go down, who really knows? While writing the statement, I needed a better answer so I created a couple quick and simple tool. If an owner can estimate a few stats, they can predict changes in plate appearances, Runs, RBI when a hitter moves from one team to another.

The key was to be simple and quick. For simplicity, only the following stats are needed.

  • New likely lineup location
  • Estimate of projected home runs
  • Estimated games played such 150 out of 162 games as a percentage.
  • Estimated Runs scored by a team. Used over on-base percentage because team level runs scored is easier to find and remember.

The estimated runs scored is the toughest value to come up with. I’d just go to FanGraphs team projection page to get a decent idea. Just take that year’s RS/G and multiply it by 162. Another method is to take the previous season value and plug it into the following regression equation:

`RS in Y2` = .575 * `RS in Y1` + 311

The goal is just to get a basic idea of possible changes.

The lineup position, estimated games played, and projected runs scored by a team can be plugged into the following equation to get a plate appearance estimate. (Spreadsheet for download):

Estimated PA = 663 – (18*Lineup spot) + (.158*Team Runs Scored)

Now with the plate appearance estimate automatically generated, the home run estimate needs to be included and then just reference the lineup spot for the Runs and RBI estimate. I tried adding several other factors like increasing the expected values depending on team offense. A most, I could only get a 1 RBI or Run change. The one factor I did notice cause values to be off was home runs. I created a the non-HR totals and then add the home runs to both stats.

Now it’s time to go and work through the Chase Headley example. Let’s assume Headley stayed with the Yankees he’d hit 7th in their lineup while he’ll hit 4th with the Padres. Using our projected runs scored, the Padres are at 667 runs scored and the Yankees at 831 runs. Assuming he’ll miss a game every other week for 25 games total or 85%. Additionally, he’ll hit 12 home runs in each instance.

Here Headley’s Yankees stats:

And with the Padres.

By moving up the lineup, Headley gets the additional PA for batting higher. The plate appearances along with being surrounded by a team’s best hitters make him a better fantasy asset.

Change in Chase Headley’s Production After Trade
Team PA Runs RBI
Yankees 569 57 73
Padres 593 64 87
Change 24 7 14

Instead of being unsure how his value changed, it seems to be a positive for him going to the Padres (if my assumptions were true).

This tool is simple and it can quickly find a potential change in production for a hitter on the move. By entering a total of four factors all the desired values get calculated. As always, let me know if there is any way to improve it for future use.

Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR once, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.

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4 years ago

Great work as always. Always been weird to me how overlooked R and RBI is in fantasy analysis

4 years ago
Reply to  stockhfcrx2

well they are completely dependent on lineup placement & quality of teammates and #plate appearances.

Which means its a crapshoot since we all value the players who get more PA anyhow, the only other factors to consider are abstract