The American League MVP Award is given out each year to the most outstanding player in that league. Outfielder Mookie Betts of the Boston Red Sox took home the award last year with an incredible season. That said, Mike Trout, who many consider the best player of the current generation, is the current odds-on favorite to win in 2019.
Below you will find the latest odds to win the AL MVP along with some advanced metrics for each player last year. We’ve included each players’ Weighted Runs Created Plus (wRC+) as well as their Wins Above Replacement (WAR) from the previous season. Below the odds you will find more detailed information about these metrics. A player with a DNQ (Did Not Qualify) for their wRC+ last year simply means they did not have enough plate appearances to accurately assess their performance.
The final column represents the implied probability of each player winning the award given the current market odds.
MVP odds courtesy of BetOnline
| Player | Team | 2018 WRC+ | 2018 WAR | Odds | Change | MVP |
|---|---|---|---|---|---|---|
| Mike Trout | Angels | 191 | 9.8 | +250 | New | 21.53% |
| Mookie Betts | Red Sox | 185 | 10.4 | +600 | New | 10.77% |
| Aaron Judge | Yankees | DNQ | 5.1 | +800 | New | 8.37% |
| Alex Bregman | Astros | 157 | 7.6 | +900 | New | 7.54% |
| Carlos Correa | Astros | DNQ | 1.7 | +1600 | New | 4.43% |
| Giancarlo Stanton | Yankees | 127 | 4.2 | +1600 | New | 4.43% |
| Jose Ramirez | Indians | 146 | 8 | +1600 | New | 4.43% |
| J.D. Martinez | Red Sox | 170 | 5.9 | +1800 | New | 3.97% |
| Francisco Lindor | Indians | 130 | 7.6 | +2000 | New | 3.59% |
| Jose Altuve | Astros | 135 | 4.9 | +2000 | New | 3.59% |
| Matt Chapman | Athletics | 137 | 6.5 | +2500 | New | 2.90% |
| Khris Davis | Athletics | 135 | 2.6 | +3300 | New | 2.22% |
| Matt Olson | Athletics | 117 | 3.4 | +3300 | New | 2.22% |
| Nomar Mazara | Rangers | 96 | 1 | +3300 | New | 2.22% |
| Gary Sanchez | Yankees | DNQ | 1.7 | +4000 | New | 1.84% |
| George Springer | Astros | 119 | 2.9 | +4000 | New | 1.84% |
| Jose Abreu | White Sox | 114 | 1.2 | +4000 | New | 1.84% |
| Andrew Benintendi | Red Sox | 122 | 4.4 | +5000 | New | 1.48% |
| Byron Buxton | Twins | DNQ | -0.4 | +5000 | New | 1.48% |
| Vladimir Guerrero Jr. | Blue Jays | Rookie | Rookie | +5000 | New | 1.48% |
| Xander Bogaerts | Red Sox | 133 | 4.9 | +5000 | New | 1.48% |
| Chris Sale | Red Sox | Pitcher | 6.1 | +6600 | New | 1.12% |
| Corey Kluber | Indians | Pitcher | 5.4 | +6600 | New | 1.12% |
| Justin Smoak | Blue Jays | 121 | 1.7 | +6600 | New | 1.12% |
| Justin Upton | Angels | 124 | 3.1 | +6600 | New | 1.12% |
| Whit Merrifield | Royals | 120 | 5.2 | +6600 | New | 1.12% |
| Miguel Cabrera | Tigers | DNQ | 0.7 | +10000 | New | 0.75% |
wRC+ is one of the best metrics we have in evaluating the impact a player has on their team. We have provided the formula for this stat below, but the idea behind it is to normalize how many runs a player creates for their team adjusting for external factors like ballpark or era. A wRC+ of 100 indicates a league average player, meaning a batter with a wRC+ of 150 is 50% better than the league average at doing the most important thing he can for his team – creating runs.
(((wRAA per PA + league runs per PA) + (league runs per PA - ballpark factor x league runs per PA) / league wRC per plate appearance, excluding pitchers)) x 100
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