Projecting the Impossible: Pitcher Wins

In the latest episode of the Launch Angle Podcast, Rob Silver asked me how many Wins did I expect Chris Archer to accumulate this season. Basically, I came back with my normal response, I don’t chase Wins and don’t care. He pushed a little harder and wondered the actual difference. I just stammered out a horrible response because I didn’t know. I’m not one to not know so found out with the answer being a win or two.

For years, I’ve used the potential for more Wins as a tie breaker between pitchers with similar baseline stats (strikeouts, walks, and groundball rate). I focused on talent first. Usually, I found pitchers on projected better teams being drafted way ahead of those with similar skills on worse teams. I just assumed the better skills will lead the pitcher to as many Wins as the worse pitcher on a better team. There is no need for me to make that assumption anymore.

The data sources are now available to see how many Wins a pitcher is projected to win knowing his projected ERA and his team’s projected win percentage. For the projections, I used Steamer projections. For the preseason projected winning percentage for each team, I used the CAIRO projections from the Replacement Level Yankees Weblog from 2010 to 2015 and our FanGraphs projected standing from the past two seasons.

Then, I matched up projected starters (GS >= 20, GS/G >= .95) and the teams with the actual results. I dropped the requirements down to 10 GS and GS/G to 50% to account for injuries and teams not giving bad starters many starts. In all, I matched up 861 starters.

The first test I ran was a linear regression with the projected ERA and team win% to the pitcher’s actual Wins per games started. The r-squared ended up at 0.15 (0.11 with just ERA). Not good or surprising considering all the factors involved. The equation works out to:

Actual Pitcher Win% = .282-0.0585*proj ERA+.646*proj team win%

Actual Pitcher Win% Knowing ERA and Team Win% Projections
Team Win%/ERA 3.00 3.50 4.00 4.50 5.00
.400 29.4% 29.3% 29.2% 29.1% 29.0%
.425 31.2% 31.1% 31.0% 30.9% 30.8%
.450 33.0% 32.9% 32.8% 32.7% 32.7%
.475 34.8% 34.7% 34.7% 34.6% 34.5%
.500 36.7% 36.6% 36.5% 36.4% 36.3%
.525 38.5% 38.4% 38.3% 38.2% 38.1%
.550 40.3% 40.2% 40.1% 40.0% 39.9%
.575 42.1% 42.0% 41.9% 41.8% 41.7%
.600 43.9% 43.8% 43.7% 43.6% 43.6%

While these values give the Win%, it’s not an easy number to work with. Instead, here are the Win totals given 32 starts.

Actual Pitcher Wins Prorated to 32 Starts Knowing ERA and Team Win% Projections
Team Win%/ERA 3.00 3.50 4.00 4.50 5.00
.400 9.4 9.4 9.3 9.3 9.3
.425 10.0 10.0 9.9 9.9 9.9
.450 10.6 10.5 10.5 10.5 10.5
.475 11.1 11.1 11.1 11.1 11.0
.500 11.7 11.7 11.7 11.6 11.6
.525 12.3 12.3 12.3 12.2 12.2
.550 12.9 12.9 12.8 12.8 12.8
.575 13.5 13.4 13.4 13.4 13.4
.600 14.1 14.0 14.0 14.0 13.9

Now, there is quite a bit of difference for teams on the extreme ends with about 5 Wins separating the same pitcher on a team projected with a .600 winning percentage vice one at .400.

Using linear regression, the team winning percentage is more of a factor than talent in the number of Wins a pitcher gets.

Besides linear regression, I just bucketed the data in 0.25 ERA blocks with either a projected winning or losing record.

First, here are the average and median values for each pitcher block.

Average Results

Actual Pitcher Wins & Winning% Grouping by ERA and Team Win% Projections
Winning% Wins w/ 32 GS
ERA Winning Team Losing Team Winning Team Losing Team
3.00 or less 54% 53% 17.2 16.9
3.01 to 3.25 45% 40% 14.3 12.8
3.26 to 3.50 44% 38% 14.2 12.1
3.51 to 3.75 42% 38% 13.3 12.0
3.76 to 4.00 39% 34% 12.5 10.7
4.01 to 4.25 38% 33% 12.0 10.6
4.25 to 4.50 38% 32% 12.2 10.3
4.51 to 4.75 35% 32% 11.2 10.3
4.76 to 5.00 30% 30% 9.7 9.7
5.01 or more 36% 29% 11.6 9.1

Median Results

Actual Pitcher Wins & Winning% Grouping by ERA and Team Win% Projections
Winning% Wins w/ 32 GS
ERA Winning Team Losing Team Winning Team Losing Team
3.00 or less 57% 53% 18.1 17.0
3.01 to 3.25 44% 42% 14.1 13.3
3.26 to 3.50 45% 37% 14.4 11.8
3.51 to 3.75 40% 37% 12.8 11.7
3.76 to 4.00 39% 33% 12.6 10.7
4.01 to 4.25 38% 32% 12.2 10.3
4.25 to 4.50 38% 30% 12.0 9.7
4.51 to 4.75 36% 32% 11.6 10.4
4.76 to 5.00 28% 31% 9.0 9.9
5.01 or more 35% 29% 11.2 9.1

The difference is around 1.5 Wins between being on a winning or losing team. This difference is about the same as the difference between .475 and .525 teams in the linear regression model.

The value I find most interesting is that around a pitcher with a 4.50 projected ERA on a projected winning team will get as many Wins as a 3.50 projected ERA pitcher on a projected losing team.

Let’s use this information to examine Archer. He’s projected for several ERA’s (3.27 to 3.58) so I’ll assume a 3.50 ERA for this example. The Rays are projected for a winning%, at .486. Here are his projected Wins from his projections and the above process.

Source: Wins (per 32 GS), Win%
Averaged projections: 13.8, 43%
Linear regression: 11.4, 36%
Average binned: 12.0, 38%
Median binned: 11.8, 37%

Hold while I try one more method before I compare the results. I took the pitchers with a projected ERA from 3.25 to 3.75 with a team projected win% between .456 to .513. In all, 23 pitchers were in the sample and averaged a 41.1% Win% (median was 40.6%) with the Wins working out to 13.2 for the average value (median was 13.0).

It’s a range from 11 to 14 Wins for Archer. If on a projected winning team, he’d be projected between 12 and 16 wins. There is a lot of projection going on but that’s all we can really do at this point in the season.

Overall, I expected the Win total difference to be smaller depending on the if the team is projected to have a winning or losing record. The difference works out to about 1.5 Wins and can grow or shrink a bit depending on the team’s projected win%. For me, I’d be looking at the extremes (e.g. Astros, Royals, Marlins, and White Sox) to find those pitchers who could break the norm. Most teams are near the middle so we need to project an average number of Wins from them.



Print This Post

Jeff 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 three FSWA Awards including on for his MASH series. In his first two seasons in Tout Wars, he's won the H2H league and mixed auction league. Follow him on Twitter @jeffwzimmerman.

newest oldest most voted
tb.25
Member
tb.25

What sort of error bars are there on these?

It’s interesting that, at least for the regression, perhaps team winning percentage is acting as a somewhat proxy for team defense and/or offense.

jimcal
Member
Member
jimcal

> Actual Pitcher Win% = .282-0.0585*proj ERA+.646*proj team win%

Great, my Gerrit Cole + Charlie Morton should have 40 wins upside.