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You see a ball that has been hit full force, going backwards, backwards, backwards. You listen this. It’s a home run. How could it be otherwise? Let’s go. It just has to be…until it dies on the runway, nestled comfortably in the glove of a waiting flyer.
If you’ve watched, oh, three rounds of this MLB season, you’ve seen this scenario play out at least once.
Bullets that look like they should be hits end up as outs instead. This is evident in the frustrations expressed by some players and coaches, who shared their doubts about the quality of baseball itself. But it’s even clearer in the statistics: Specifically, the expected statistics, Statcast numbers such as xBA and xSLG that show what a batter’s performance “should” be based on batted ball data. On an individual level, a discrepancy between expected and actual stats can tell us about an underperforming or overperforming player, and what we should expect moving forward. At the league level, however, there’s usually not much of a difference: in such a large sample, what you “should” see is usually more or less what you actually do.
This year looks a little different right now.
Looked. Here’s how the expected league-wide stats compared to the actual stats over the past few seasons:
At a Glance: Typically, there’s little difference between what the data predicts and what’s actually happening at league level. Yet this year there are relatively huge a. (The difference between actual and expected batting average this season equals the difference between what would be the worst league offense in history and what would be the best in a few years.) But it’s not that simple! Here’s a breakdown of what we can — and can’t — know from the stats expected so far.
Is this really the biggest discrepancy we’ve seen between expected and actual stats?
This may seem obvious from the table above: Yes, duh, the gap is huge. But the real answer is… sort of, maybe, but not quite.
For context, these expected stats come from taking basic information about an individual batted ball, such as exit velocity, launch angle, and the stage it was hit, and comparing it to the data. past to determine the most likely outcome. But the key bit here is the data used to make these comparisons. The league calculates the baseline for this twice a year, according to MLB statistics analyst Mike Petriello: once at the All-Star Break and once more after the season ends. It means that before the All-Star Break – like, say, right now! – the baseline actually comes from past years.
Yes, this is all very wonky, (literally) inside baseball stuff. But it is crucial to understand what these numbers say. In other words: when you see there was virtually no league-wide discrepancy between expected and actual stats in 2021 and 2020, you see there was virtually no discrepancy once the baselines were adjusted to reflect the attacking environment of those years, which hasn’t happened yet for 2022. In a world that hasn’t seen dramatic year-on-year fluctuations in attacking environments, that wouldn’t mean much. Unfortunately, MLB is clearly not in this world. If there is a big change in the offensive environment from year to year, for example, if baseball itself is different, the numbers will show significantly different things.
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The result? You cannot make a direct comparison at the moment between the expected statistics for this season and the previous ones. They simply reflect different contexts. Last year’s data is set to match the specific circumstances of last year, and this year’s data is, well, also set to match the specific circumstances of last year. At least until July.
(If you’re wondering why MLB isn’t updating the baseline to reflect the current environment sooner? It’s been discussed, Petriello says, but it would be potentially confusing to have numbers changing mid- The All-Star break and the end of the season provide natural points for this kind of redirection.)
So what are the expected stats really telling us right now?
Still many ! When you look at an actual league batting average of .233 versus an expected batting average of .253, what you’re seeing is the actual performance this year versus what that same performance should have produced in the environment offense last year.
Which tells you that this year’s environment is really, really different.
If it was unchanged from last year? With this quality of contact from the batters, the ball would fly a lot more and there would be no serious conversations about why the offense fell apart. The league batting average would be over .250 and the slugging would be over .430! Yet the same contact in this the environment leads to considerably lower offensive numbers.
Why? To what extent could this be defensive positioning?
Speaking of environment: what about change, which is more popular than ever this year? Sure, that wouldn’t affect potential home runs that are now deep fly-outs, but it can do a lot to turn hits into outs elsewhere on the field, can’t it?
Yes, but the numbers don’t suggest that’s a meaningful answer here. There is a significant discrepancy between actual and expected batting averages for each in-field lineup: standard (.240 vs. .257), strategic or partial change (.238 vs. .249), and complete change (.222 vs. .247) . The difference is obviously greater when teams use a full shift – defined as three infielders to one side of second base – but it’s still quite significant when teams are in a standard infield lineup. In other words, the discrepancy between actual and expected batting average is not due to teams optimizing for change. It seems to transcend that.
What else could be causing this?
Well, there’s no way to tell definitively. But given that there haven’t been any dramatic, large-scale changes from last season, either in the player base or in the dimensions of baseball stadiums across the league… baseball itself- even remains the main culprit. When talking about something that affects all of MLB, with such a noticeable change from year to year, it’s hard to find any other plausible answer.
What does this mean in practice?
Simply put, a lot of bullets that you might imagine to be shots are now out. There have been 33 balls this season that ended in outs despite having an xBA over 0.950, including one with an xBA of 1.000. Yes, a bullet with an exit velocity and launch angle that had been hit in any other past context… taken for a fly-out.
An outing with a perfect xBA? What does it look like??
The apology to Ronald Acuña Jr. Ball may not lie, but while it changes from season to season, it doesn’t feel entirely truthful.
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