The American League Cy Young Award is given to the best pitcher in the AL 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.
Blake Snell of the Tampa Bay Rays was a somewhat surprising winner of the award in 2018. This year Snell is given just the fifth best odds in the field to take home the Cy Young with the Indians Corey Kluber currently listed as the odds-on favorite.
Below you will find the latest odds to win the AL 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 |
|---|---|---|---|---|---|---|
| Corey Kluber | Indians | 3.23 | 5.4 | +260 | New | 21.22% |
| Chris Sale | Red Sox | DNQ | 6.1 | +325 | New | 17.97% |
| Luis Severino | Yankees | 3.26 | 5.5 | +600 | New | 10.91% |
| Justin Verlander | Astros | 2.63 | 6.7 | +1400 | New | 5.09% |
| Blake Snell | Rays | 3.3 | 4.9 | +1600 | New | 4.49% |
| Carlos Carrasco | Indians | 3.03 | 5.3 | +1600 | New | 4.49% |
| Gerrit Cole | Astros | 2.91 | 5.9 | +1600 | New | 4.49% |
| Trevor Bauer | Indians | 3.21 | 5.8 | +1600 | New | 4.49% |
| David Price | Red Sox | 3.82 | 2.4 | +2000 | New | 3.64% |
| James Paxton | Mariners | DNQ | 3.7 | +2200 | New | 3.32% |
| Jose Berrios | Twins | 3.8 | 3.2 | +2800 | New | 2.63% |
| Masahiro Tanaka | Yankees | DNQ | 2.5 | +2800 | New | 2.63% |
| Yusei Kikuchi | Mariners | Rookie | Rookie | +3300 | New | 2.25% |
| Rick Porcello | Red Sox | 3.77 | 2.6 | +4000 | New | 1.86% |
| Andrew Heaney | Angels | 3.74 | 2.7 | +5000 | New | 1.50% |
| Charlie Morton | Astros | 3.52 | 2.9 | +5000 | New | 1.50% |
| J.A. Happ | Yankees | 3.64 | 3.1 | +5000 | New | 1.50% |
| Marcus Stroman | Blue Jays | DNQ | 1.4 | +5000 | New | 1.50% |
| Michael Fulmer | Tigers | DNQ | 1.3 | +5000 | New | 1.50% |
| Mike Clevinger | Indians | 3.86 | 4.2 | +5000 | New | 1.50% |
| Nathan Eovaldi | Red Sox | DNQ | 2.2 | +5000 | New | 1.50% |
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