Mike Trout, sabermetrics and the end of baseball.

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I’m not sure which generation of baseball fans I belong to.

I know I’m old enough to have listened to the ’88 World Series and regular season games on a shortwave radio that (I don’t know why) my dad had, which made me appreciate and love radio broadcasts, as in the place where we lived there was no good reception of the very few open television channels that broadcasted them. On the other hand, today I cannot conceive of watching a game without having a real-time view of the N amount of statistics that we have available today.

I can recite from memory how many times Rickey Henderson was the leader stealing bases (6) or how many times Tony Gwynn was the batting champion (8) and I love those stats even though Stolen Bases and Batting Average seem irrelevant to MLB and world baseball teams today.

I also learned that OBP (On-Base Percentage) changed baseball forever and that at .482, Ted Williams has the highest for a career and that the active player closest to him is Joey Votto with a distant, but very remarkable .421, above one Mickey Mantle (as of Aug 7, 2020).

I move between those two waters and that truly seems fine to me, but I understand that not everyone feels the same.

What is difficult for me sometimes to understand is that we deny ourselves the possibility of complementing our ideas and ways of seeing the game in order to maintain others that, without realizing it, have evolved within the best leagues in the world at an unstoppable rate.

I am bringing this topic up because a few days ago I had a very pleasant interaction about someone who is a controversial subject in that part of Twitter that talks about baseball and especially in our Latin American countries: Mike Trout.

Due to some circumstances, Trout is looked down upon almost daily in various circles, even specialized ones, and is the subject of innumerable statements that seek to minimize what the New Jerseyan do in the ballparks, especially in comparison with other players who do not suffer from these appreciations.

In this specific case, they were talking about the MVP that Trout won in 2016 over Mookie Betts and how, according to the appreciation of an interesting group of people, this had been a blatant robbery against Betts.

The main argument was in the following comparison:

A good comp?

The issues of the Gold Glove and the postseason deserve a separate post because their discussion goes a long way, but I can anticipate that what they indicate is more beyond the player’s performance than we might think of.

I am going to expand here what I commented about the numbers in the image.

Let’s start with the data that originates the discussion: the statistical comparison between Trout and Betts for 2016. That year Trout wins the MVP for the AL by about 12% points of difference, the product of the vote.

By traditional analysis, the numbers shown give Betts an advantage in homeruns (HR) by 2; RBIs by 13; hits (H) by 41; doubles (2B) by 10; and batting average (AVG) by 0.003. If we assume that there is a technical tie at AVG, Betts beats Trout in 4 categories.

Trout wins on runs (R) by 1; stolen bases (SB) by 4; and tie in triples (3B). Let’s say the Rs are a technical tie, and then he just wins on SB. Traditional statistics 4 to 1 in favor of Betts.

Now, what do those numbers tell us?

They hit almost the same number of HRs, so that doesn’t clarify anything, for example.

Betts drove in more runs, and since they have a nearly identical AVG, then we can deduce that he was lucky enough to get more people on base when he went to hit – that’s not up to him or Trout. Boston that year was the best team getting on base (OBP .348) while the Angels ranked a much lower 14th.

This is one of the reasons why RBIs have lost relevance in modern batter analysis: they are the result of events (people on base + hits) in which 50% do not depend at all on the batter; he can influence what he’s capable of doing in his at-bat, but if there are no runners on base, it doesn’t matter what he does, unless it’s a HR. Of course, RBIs have been used so much through the years that they are tattooed into our collective wisdom of baseball.

Something similar happens with runs scored: they are the result of an event in which half of it (someone has to bring me home) is not up to me. The RBIs and Rs speak both of the lineup around me as of my own capabilities.

AVG also becomes irrelevant and I briefly name two reasons why : it totally ignores other forms of ways to get on base (mainly walks) and treats all hits the same (1 infield hit “is worth” the same as 1 HR).

That is totally absurd as it minimizes the impact of the different types of hits. Mind you, I don’t mean to say that AVG is useless, of course a .360 hitter should be better than a .200 hitter. What happens is that it does not give as much or better information than other statistics and especially when the differences are small, as in this case, that matters a lot.

Where Betts’ advantage is indisputable is in the bigger number of Hits, which, of course, influences the greater number of 2B although it’s worth noting that it was in about 50 more Plate Appearances (PA).

So, what should we evaluate?

I am going to share the following numbers for both players during that same year. There are 11 statistics with different levels of progress that evaluate performance more thoroughly and better. In reviewing them, Trout leads Betts by 8 out of 11.

OBP can be called as the great revolutionary of modern baseball: it is about the percentage of times a batter gets on base, and if you had the chance to see the Moneyball movie, you will remember that Oakland began to use it to look for players who would get on base a lot since it has been determined that this is one of the most determining factors in runs production, which is ultimately what wins games.

That year, 2002, Oakland against all odds and with one of the most ridiculous payrolls in baseball put together a highly winning team, although they fell short in the end.

All teams have then adopted this evaluation method and have elaborated on it extensively with other a miriad of others, too. OBP is very straightforward, but it gives a better indication of the hitter’s ability than the AVG does. Trout was over 20% better there than Betts, a very marked difference.

Slugging (SLG) lets you know the impact of a batter’s hits; Since an infield hit is not the same as a HR, we can use this formula to approximate the magnitude of the type of hits he produces. The formula is simple: singles “are worth” 1 point, doubles 2, triples 3 and home runs 4; they are added together and divided between at-bats.

(1B + 2Bx2 + 3Bx3 + HRx4)

Here, too, Trout has an advantage, indicating that each time he had an official at-bat, his hits were “more impactful” in proportion. But one can then wonder: if he had fewer hits, fewer doubles, and fewer HRs, how is that possible?

The key is precisely that he did it in fewer official AB and, consequently, in the walks he received.

Trout had 49 fewer appearances at the plate than Betts, which is quite a lot, but remember that we use official turns (AB) because walks and sacrifices (among others) do not count for it; then the difference becomes brutal: Trout had 549 vs. 672 AB of Betts.

This means that when Trout did indeed put the ball into play, the rate of his hits were of a more significant magnitude. And the times he didn’t have a legal turn he did something just as important: he walked.

Looking at his BB%, which is the percentage of turns that ended up being a walk, Trout tripled Betts there resulting in him receiving 116 walks to just 49 for Betts. That’s a frankly a mammoth relationship, and the main reason Trout outperformed Betts.

Note that, therefore, although Trout played 1 more game than Betts that season, his number of legal turns is 123 smaller than Betts, therefore, it means that he was proportionally better in the legal turns he took and when he did not put the ball in play he reached base by the way of a walk.

OBP and SLG clarify a lot but today we have a couple of stats that go much further and the consensus among the MLB teams (which in the end are the ones who know the most about this game) is that wOBA and wRC + are highly descriptive of a hitter’s performance.

It should come as no surprise then that Trout that year literally outpaced Betts in those stats. I recommend this link where they resume them much better than what I can: library.fangraphs.com/offense/wrc/.

A tip: a WRC + of 136 is considered above average, like Betts’ that year. One of more than 160 is considered Excellent: Trout had 176. Not surprisingly, Trout’s fWAR was significantly better than Betts’s that year; there are well-supported and factual reasons that justify it.

About fWAR, FanGraphs’ WAR, we can discuss later.

Finally, I wanted to share an idea that I heard in a fabulous conversation between @IgnacioSerrano and @nelsonmatamoros, in which they talked (among many other interesting things) about the adversity to advanced statistics from us baseball fans, and compared it to when someone prefers to use an analog push-button phone, and so on; that person has every right to use it and it will probably serve him within the limitations to communicate with someone.

However, because you do not want to use a more advanced phone, you will probably not be able to hear the voice better or see the image of the other person, missing a huge part of the experience, perhaps the best. That person may continue to be happy because despite this he manages to communicate, but unfortunately, he is not aware of what he is missing from appreciating.

This is how I compare the experience of enjoying baseball today: we can settle for seeing it as they did 50 years ago and that’s not inherently bad, but if we motivate ourselves to evolve with it, at our own pace, but without stopping, it is very likely that we will enjoy it even more.

Sabermetrics is not the end of baseball at all, as many like say; baseball has changed a long time ago, and perhaps we did not realize it and many more changes will come. Baseball will always be there waiting for us.

All data used was taken from https://www.fangraphs.com/https://baseballsavant.mlb.com/, and/or https://www.baseball-reference.com/, unless otherwise stated different.

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