Introducing a new and improved pwOBA+ (Pitchers)

In the last week, I’ve introduced three new and improved pitching metrics: pwOBAcon+, pBB%+, and pK%+. All three of these stats feed into predictive weighted on-base average plus (pwOBA+).

One other component that goes into pwOBA+ is regressed hit-by-pitch percent.

pwOBA = (pBB%*wBB)+(HBP% regressed*wHBP)+((1-HBP% regressed-pBB%-pK%)*pwOBAcon)

pwOBA+ = pwOBA/league average non-bunt wOBA*100

Here are the lowest single-season predictive weighted on-base average plus marks since 2015

1. Dellin Betances 2016 (79.9)
2. Clayton Kershaw 2015 (80.4)
3. Gerrit Cole 2019 (80.9)
4. Andrew Miller 2016 (81.5)
5. Craig Kimbrel 2017 (81.8)
6. Josh Hader 2019 (82.4)
7. Clayton Kershaw 2016 (83.3)
8. Chris Sale 2018 (83.3)
9. Dellin Betances 2015 (83.7)
10. Dellin Betances 2017 (83.7)

Highest

1. Chris Tillman 2017 (112.6)
2. Ariel Jurado 2018 (112.2)
3. Bryan Mitchell 2018 (112.0)
4. Kyle Kendrick 2015 (111.5)
5. Chris Tillman 2018 (111.5)
6. Andrew Cashner 2018 (111.2)
7. Dan Straily 2019 (111.1)
8. Brian Flynn 2018 (110.8)
9. Bartolo Colon 2018 (110.7)
10. Jacob Nix 2018 (110.5)

In looking at how predictive 2018 pwOBA was of 2019 non-bunt wOBA compared to 2018 non-bunt wOBA and 2018 non-bunt xwOBA, one can clearly see that pwOBA is far more predictive of future non-bunt wOBA than non-bunt xwOBA and non-bunt wOBA are. It is also the most stable of the three metrics.

The same conclusions can be said about pERA (pwOBA on the earned run average scale) in comparison to the other ERA estimators.

If one wishes to have an idea of which players one would predict to pitch best moving forward, one should take note of the leaders in pwOBA/pERA.

Featured image- Creator: Harry How | Credit: Getty Images