![]() ![]() Judge isn’t going to get the same amount of plate appearances as Alonso (or any hitter, for that matter). Judge recently received a second injection in his toe, which should help speed up the recovery - and it is believed that Judge can be back in about a month. *On Injured List –– Odds provided by FanDuel Sportsbook However, Judge’s toe injury throws a wrench in our analysis. So, from this perspective, Judge is clearly the better bet to lead the league in home runs this season. Ultimately, we can project home run regression for Alonso - he should hit fewer on a per-pitch basis going forward - which we can’t necessarily project as much for Judge. Judge has slightly overperformed, too, given he’s only recorded 17.5 expected home runs to his 19. In fact, it’s only 1.2 more expected home runs than Judge. ![]() Per Baseball Savant, Alonso has hit 3.4 more home runs (22) than expected home runs (18.6), the largest difference between any batter with at least 15 home runs this season. Getty Imagesīaseball Savant tracks these differences to record a stat called expected home runs, which accounts for ballpark and environmental variances to help more accurately compare home run totals across hitters. Shohei Ohtani is the MLB’s do it all star, leading baseball in home runs with 24. We know every ballpark is slightly different, so a long fly ball could be a home run at one ballpark and a flyout at another. ![]() However, there’s a reason to believe Alonso’s been slightly lucky, at least from a home run perspective. He recently smacked a homer in back-to-back games against the Blue Jays and Braves. However, if you were to place your hard-earned money on only one of the New York superstars, which one should you pick?Īlonso’s smacked 22 home runs so far this season. Other names are in the mix, including Atlanta’s Matt Olson, Miami’s Jorge Soler, and Philadelphia’s Kyle Schwarber. ![]() He’s one ahead of the Mets’ Pete Alonso and four ahead of the Yankees’ Aaron Judge. Even if 2.4% isn’t a huge amount in the abstract, it’s enormous in the context of the sheer difficulty.Get the free Action Network app for expert picks, live odds, bet tracking and more.Īfter an incredible hot streak, Shohei Ohtani stands alone atop the home run leaderboard with 24. Of course he didn’t! And the dead ball should only have lowered those odds. If I told you Judge had a 1-in-40 shot of hitting 70 homers before the year, you’d laugh at me. On the other hand, it’s a phenomenal achievement anyway. With the changes in my approach, he gets there in roughly 2.4% of simulations. With this method, I feel more confident in saying that 70 is a long shot, even with the way Judge is playing. To hit six homers in 14 games, Judge both needs to be in good form and likely needs almost all of those 14 games. That works out to a 32.4% chance, and if you’re going to witness that home run in person, you’ll want tickets to the last series of the season, naturally enough. Here, for example, are the days when Judge might hit his 66th homer, tying Sammy Sosa on the single-season home run list: I think this model does a better job of accounting for the odds of something remarkable happening. In each simulation, I picked an underlying home run rate talent from a distribution of possible Judge home run rates rather than just using the average. If Judge is going to hit 70 homers, it stands to reason that he’ll probably do it when he’s the best version of himself, on a personal home run tear. There’s no way of knowing in advance which of the two he’ll be, but players don’t produce at a monotonic level forever. Sometimes he might be feeling off, and only have a 6% likelihood. Sometimes, he might be feeling good and briefly have an 8% true-talent likelihood. In plain English, Aaron Judge might be 7% likely to hit a home run on any given plate appearance over the medium and long run, but that’s not necessarily the case week to week. As Dan noted in his piece on triple crown odds, picking an underlying player talent level from a distribution does a better job of reflecting reality than using a static number. That works pretty well for the center of the probability distribution, but if you’re looking for outliers (like 70 homers), it falls short. Previously, I estimated a true-talent home run rate and used that for every simulation. ![]()
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