The National League Cy Young Award is given to the best pitcher in the NL for that year. The members of the Baseball Writer’s Association of America vote on the award with one representative from each team. Each member places a vote for first, second, third, fourth and fifth among the pitchers in the National League. The formula used to calculate the final scores is a weighted sum of the votes. Obviously, the pitcher with the highest score wins the award.
Jacob DeGrom was the NL Cy Young Award winner for the Mets last year and is currently listed second behind the Nationals Max Scherzer for the best odds to win the award this year.
Below you will find the latest odds to win the NL Cy Young along with some advanced metrics for each player last year. We have included Skill-Interactive Earned Run Average (SIERA) as well as Wins Above Replacement (WAR). Below the odds you will find more detailed information about these metrics. A player with a DNQ (Did Not Qualify) for their SIERA last year simply means they did not have enough innings pitched to accurately assess their performance.
The final column represents the implied probability of each player winning the award given the current market odds.
Cy Young Odds courtesy of BetOnline
| Player | Team | 2018 SIERA | 2018 WAR | Odds | Change | Cy Young |
|---|---|---|---|---|---|---|
| Max Scherzer | Nationals | 2.71 | 8.1 | +250 | New | 21.81% |
| Jacob DeGrom | Mets | 2.78 | 9.4 | +400 | New | 15.27% |
| Aaron Nola | Phillies | 3.4 | 5 | +900 | New | 7.63% |
| Clayton Kershaw | Dodgers | DNQ | 4 | +1200 | New | 5.87% |
| Noah Syndergaard | Mets | DNQ | 4.2 | +1400 | New | 5.09% |
| Kyle Freeland | Rockies | 4.35 | 3.9 | +2000 | New | 3.63% |
| Madison Bumgarner | Giants | DNQ | 1.5 | +2000 | New | 3.63% |
| Jack Flaherty | Cardinals | DNQ | 2.5 | +2500 | New | 2.94% |
| Miles Mikolas | Cardinals | 3.93 | 4.6 | +2500 | New | 2.94% |
| Patrick Corbin | Nationals | 2.91 | 6.2 | +2500 | New | 2.94% |
| Stephen Strasburg | Nationals | DNQ | 2.3 | +2500 | New | 2.94% |
| Walker Buehler | Dodgers | DNQ | 3.2 | +2500 | New | 2.94% |
| Zack Greinke | Diamondbacks | 3.6 | 3.7 | +2500 | New | 2.94% |
| Alex Reyes | Cardinals | DNQ | 0.0 | +3300 | New | 2.25% |
| Yu Darvish | Cubs | DNQ | 0.2 | +3300 | New | 2.25% |
| German Marquez | Rockies | 3.31 | 4.9 | +4000 | New | 1.86% |
| Zack Wheeler | Mets | 3.87 | 4.5 | +4000 | New | 1.86% |
| Chris Archer | Pirates | DNQ | 2.5 | +5000 | New | 1.50% |
| Cole Hamels | Cubs | 3.99 | 2.3 | +5000 | New | 1.50% |
| Jon Lester | Cubs | 4.57 | 1.9 | +5000 | New | 1.50% |
| Jose Quintana | Cubs | 4.39 | 1.3 | +5000 | New | 1.50% |
| Kyle Hendricks | Cubs | 4.03 | 3.2 | +5000 | New | 1.50% |
| Mike Foltynewicz | Braves | 3.77 | 3.3 | +5000 | New | 1.50% |
| Robbie Ray | Diamondbacks | DNQ | 0.7 | +5000 | New | 1.50% |
| Kevin Gausman | Braves | 4.28 | 2.3 | +10000 | New | 0.76% |
SIERA (Skill-Interactive Earned Run Average) tries to eliminate in-game factors that are beyond a pitcher’s control. It also factors in things like walk rate and ground and fly ball rates. SIERA is considered a better indicator of a pitcher’s overall ability than ERA, because ERA doesn’t account for these ball in play factors that can many times inflate a player’s ERA despite a strong pitching performance. The players with the best SIERA will be those that strikeout a lot of batters, induce grounders and popups, as well as limit their walks.
6.145 - 16.986*(SO/PA) + 11.434*(BB/PA) - 1.858*((GB-FB-PU)/PA) + 7.653*((SO/PA)^2) +/- 6.664*(((GB-FB-PU)/PA)^2) + 10.130*(SO/PA)*((GB-FB-PU)/PA) - 5.195*(BB/PA)*((GB-FB-PU)/PA)
WAR is something of a catch-all statistic that evaluates how many wins a player is worth to their team compared to a replacement player. WAR basically says, if this player did not play for this team in this season and their backup was at best and average player, how many wins would that team be expected to lose? What is great about WAR is that it also factors in a player’s fielding contributions, not just their hitting statistics.
WAR is a bit more complicated for pitchers since they have less control over how the team around them will perform in the field, but using a metric called Fielding Independent Pitching (FIP) helps give a better idea of how to fairly compare pitchers to league averages.
(Batting Runs + Base Running Runs +Fielding Runs + Positional Adjustment + League Adjustment +Replacement Runs) / (Runs Per Win)
[[([(League “FIP” – “FIP”) / Pitcher Specific Runs Per Win] + Replacement Level) * (IP/9)] * Leverage Multiplier for Relievers] + League Correction