DataBaseball
2026

2018 season · rankings

2018 MLB Pitching Leaders

56 players qualified at 162.1 innings pitched, ranked by dbWAR. Every column sorts across the full qualified set — not just the rows shown — and the whole table downloads as Parquet.

TableBattingPitching
RoleLeaguePool
Who
56 of 56 qualifiedDownload Parquet
#
1Jacob deGrom303232217.010902691.701.990.9111.231.4%16.4%5.5%26.7%487.5
2Max Scherzer343333220.218703002.532.650.9112.233.1%17.3%5.9%28.8%645.9
3Patrick Corbin293333200.011702463.152.471.0511.134.7%16.5%6.0%24.8%605.8
4Justin Verlander353434214.016902902.522.780.9012.231.6%16.2%4.4%30.4%675.5
5Gerrit Cole283232200.115502762.882.701.0312.430.9%15.3%8.0%26.5%655.3
6Trevor Bauer272827175.112612212.212.441.0911.331.5%14.2%7.9%22.9%595.1
7Aaron Nola253333212.117602242.373.010.979.527.7%13.2%7.0%20.0%734.9
8Corey Kluber323333215.020702222.893.120.999.326.9%13.0%4.0%22.3%754.7
9Luis Severino243232191.119802203.392.951.1410.326.8%13.3%5.9%22.3%714.5
10Carlos Carrasco313230192.0171002313.382.941.1310.833.1%16.5%5.5%24.0%714.5
11Blake Snell263131180.221502211.892.940.9711.034.6%15.7%9.1%22.4%714.3
12Miles Mikolas303232200.218401462.833.281.076.520.2%10.6%3.6%14.5%794.0
13Zack Wheeler282929182.112701793.313.251.128.824.7%12.2%7.4%16.7%783.7
14Germán Márquez233333196.0141102303.773.401.2010.628.4%13.5%7.0%21.2%823.6
15Mike Foltynewicz273131183.0131002022.853.371.089.925.7%11.3%9.1%18.0%813.4
16Mike Clevinger283232200.013802073.023.521.169.327.8%12.9%8.3%17.3%853.4
17Jameson Taillon273232191.0141001793.203.461.188.423.8%11.8%5.9%16.9%833.4
18Zack Greinke353333207.2151101993.213.701.088.625.9%11.7%5.1%18.6%893.1
19Kyle Freeland253333202.117701732.853.671.257.721.8%10.0%8.3%12.2%883.1
20Dallas Keuchel303434204.2121101533.743.691.316.719.8%9.0%6.6%10.9%893.1
21Marco Gonzales262929166.213901454.003.431.227.821.1%10.2%4.7%16.5%833.0
22Kyle Hendricks293333199.0141101613.443.781.157.322.1%9.9%5.4%14.4%912.8
23Charlie Morton353030167.015302013.133.591.1610.828.5%12.8%9.2%19.7%872.7
24José Berríos243232192.1121102023.843.901.149.525.9%12.4%7.7%17.7%942.5
25Zack Godley283332178.1151101854.743.821.459.328.1%12.5%10.2%13.1%922.4
26Nick Pivetta253332164.071401884.773.791.3010.327.5%13.2%7.3%19.7%912.3
27Trevor Williams263131170.2141001263.113.861.186.618.8%8.9%7.8%10.1%932.3
28Rick Porcello303333191.117701904.284.011.188.920.5%9.6%5.9%17.6%972.2
29Jhoulys Chacín303535192.215801563.504.031.167.321.3%9.2%8.9%10.7%972.2
30J.A. Happ363131177.217601933.653.981.139.824.1%11.5%7.0%19.4%962.1
31Andrew Heaney273030180.091001804.153.991.209.026.3%12.8%6.0%18.0%962.1
32Derek Holland323630171.17901693.573.871.298.923.8%11.2%9.2%14.0%932.1
33Kyle Gibson313232196.2101301793.624.131.308.226.8%12.1%9.6%12.1%992.0
34David Price333030176.016701773.584.021.149.122.9%10.9%6.9%17.6%972.0
35Mike Leake313131185.2101001194.364.141.305.816.7%8.3%4.3%10.8%1001.9
36Jon Gray273131172.112901835.124.081.359.629.1%13.4%7.0%17.6%981.9
37José Ureña273131174.091201303.984.171.186.720.2%9.6%7.2%11.1%1011.7
38Gio Gonzalez333232171.0101101484.214.161.447.824.0%10.4%10.7%9.1%1001.7
39Sean Newcomb253130164.012901603.904.141.338.824.9%11.1%11.6%11.4%1001.7
40Jake Odorizzi283232164.171001624.494.191.348.924.7%11.7%9.8%12.9%1011.6
41Tanner Roark323130180.191501464.344.271.287.320.3%9.3%6.6%12.6%1031.6
42Jake Arrieta323131172.2101101383.964.261.297.219.5%9.0%7.9%11.2%1031.5
43Kevin Gausman273131183.2101101483.924.321.307.324.7%12.2%6.4%12.6%1041.5
44Luis Castillo263131169.2101201654.304.321.228.828.7%14.3%6.9%16.4%1041.4
45Jon Lester343232181.218601493.324.391.317.420.3%9.0%8.4%11.2%1061.3
46Jose Quintana293232174.1131101584.034.431.328.220.6%8.8%9.2%12.2%1071.2
47Cole Hamels353232190.291201883.784.491.268.927.0%12.9%8.1%15.3%1081.2
48Matthew Boyd273131170.191301594.394.451.168.423.5%10.9%7.2%15.2%1071.2
49Tyler Anderson293232176.07901644.554.571.278.425.4%12.5%8.0%14.2%1100.9
50Reynaldo López243232188.271001513.914.631.277.221.3%10.1%9.4%9.5%1120.9
Reading this table

FIP estimates what a pitcher's ERA would be given only the outcomes they control directly — strikeouts, walks, hit batters and home runs. FIP− indexes that against the league, so 100 is average and lower is better. dbWAR is our own open implementation, not fWAR or bWAR. See the full derivation →