October 27th, 2024
I cruised through the opening section on “bad stats”. The other book I read covered this also so I knew what to expect. Batting average, RBI, wins, saves, and errors are all dud stats that either give misleading information or are downright counterproductive.
October 28th, 2024
The next section is on better offensive stats. I went through OBP, SLG, and OPS quickly. These are common knowledge at this point and even used by TV announcers. Note that OPS is most useful as a team stat as opposed to an individual stat. What’s nice is that this book provides some reference tables for what is bad, average, good, great, etc. The last one I read is on Runs Created. It’s the first stat covered that has a bit of a funky formula, but the point is that how many runs are, statistically, created by a batters actions. It counts the good, like hits and walks, and the bad, like caught stealing and double-plays. It is an accumulative stat, not a rate, so it’s not useful for comparing players who played a wildly different number of games. The author says this is mostly to prepare for wRC+, to come later.
Read a bit more of the section and it delves into the “deeper” stats. ISO is isolated power. It is SLG – BA, so just removes the singles and looks at extra base hitting. wOBA is the weighted on base average, a step up from OBP. As expected, it assigns weights to different events depending on how much they are likely to impact the game. wRC+ and OPS+ are a mouthful. wRC+ is a Fangraphs stat, OPS+ is a Baseball Reference stat. wRC+ is a “step up”, but they are similar. They are weighted averages that take the park and state of the game into account. The weights change per season depending on how the “environment” is, such as high hits, park x being a pitcher’s park, etc. They are centered on 100, so >100 is above average and 100 is below average. BsR is base running and the most confusing of the offensive stats. I think it’s only on Fangraphs. It’s the sum of 3 different complex equations and really one applies to data that was available after 2002. It’s like steals and caught steals, extra bases, GIDPs 0 is average, positive is good, negative is bad. Lots of math, but it’s used in WAR.
October 29th, 2024
The next section is on pitching and defensive stats. This is where it gets tricky to separate out the individual from the team. The batter is always alone, so that part was easy. The first is ERA+, which takes the ERA stat and normalizes it based on park location and the average ERA of the season. A low ERA when everyone has a low ERA is less valuable than a low ERA in a high run era. I think this is on Baseball Reference. I like WHIP, or Walks & Hits per Inning Pitched. It’s like OBP in that it’s good and simple, but loses some depth for clarity. It’s pretty self-explanatory. GSs, Game Score, is not a “real” stat but a fun one. It sums up a pitchers “events”, such as outs and Ks, subtracts Hs and BBs and whatnot and comes up with a score. A perfect game of 9 innings would be a minimum of 87. A perfect score with a perfect game and all Ks would be like 114. Some have gotten negative scores. Seems like a fun math game, however scores will decrease in the modern era of 6 inning pitchers.
October 30th, 2024
The pitching continues with FIP, field independent pitching. This stat looks at things that really only the pitcher can control: HRs, Ks, BBs. It is then added to a “constant” that changes seasonally, which puts in on an ERA-like scale. Like ERA, lower is better. Though the pitcher has some control over whether a hit turns into a grounder or a fly, the data shows that what turns into a hit is pretty random. Plus it takes out the hurting or helping done by the fielders. It’s good for comparing with ERA. There are some other stats listed, like Fly Ball%, K%, K/BB, etc., that are all pretty self-explanatory.The main (if not only) defensive stats that matter are DRS, defensive runs saved, and UZR, ultimate zone rating. They’re essentially the same thing, with UZR being a slightly modified DRS. DRS comes from a company that was filming games and selling the data. DRS essentially looks at a play and what percentage of players can make that play. If 20% of players make that play, you get 0.8 points for getting it and -0.2 points for missing it. 0 is average, positive is a good player, negative a bad player. Errors can mask a good player getting near balls that a bad player wouldn’t even come close to. Then there’s a section about some other odds and ends stats. The one I’m noting is DEF, defensive runs above average. It’s essentially DRS, but DRS is only comparable between people who play the same position. Not all positions are equally difficult. DEF corrects that and makes it position agnostic.
October 31st, 2024
Next section is about team stats. First up is pretty simple, DIFF or Run Differential. You take the runs scored by a team minus the runs given up by a team. This can be used to track the teams winning percentage and see if they’re inflated by small wins. A related stat, “Pythagorean Theorem”, or I’ll call it expected win percentage, is runs scored^2/(runs scored^2 + runs allowed^2). A team that wins by a lot of runs will generally win more games than one that wins by one or two, and these numbers will show you how to expect a season to go. Next is SRS, or Simple Rating System. I think this was just Diff divided by games or something and minus a SRS factor, which is based on how easy or difficult a team’s schedule is. Two teams with the same winning percentage but different SRS can tell you which one may actually be the better team. Last for today is DER, Defensive Evaluation Rating. This one is like a way to see how good the defense is at dealing with balls batted in. I guess you can think of it as hits per out or something like that. This can help with comparing pitchers, who have similar ERA but one could be aided by better defense. Likewise the pitcher can affect DER by the way some balls are hit and how hard.
November 1st, 2024
There’s two more team stats and then the final section, which I forget what the tying theme is. The first top is Win Probability, which I hate. It’s very annoying to watch a game and see that number jump around inconsistently. If it can jump from 10% to 90% from one hit, then it’s a piss-poor stat that doesn’t tell you anything. Maybe it’s fun for some, but I don’t like it. The next one is not really a stat but I will put it in the fun category: the Magic Number. How many games does the #1 team need to clinch the division compare? That’s all there is to it. It’s only really “fun” if your team is the one who is doing the winning. The last section starts with BABIP or batting average on balls in play. This is similar to OBP but only for balls that go into play, thus ignoring walks, strikeouts, and home runs. The average hovers around .300. This stat can also be used for pitchers to see how the offense does against them. A high/low BABIP season is generally abnormal and will typically return to close to the mean in following seasons. It’s not all luck though, some guys just have high BABIP based on their batting skill. Derek Jeter had a .350. It’s also dependent on the park and other defensive factors, but BABIP doesn’t take those into account.
November 3rd, 2024
Finished the book over the weekend. There were only a few more stats left. There was xBA, xSLG, and most importantly xwOBA. That is the expected version of the standard stat, meaning what SHOULD have happened based on the way the ball was hit. It essentially removes luck. If a hit would have been a hit most instances but a rare catch was made, you wOBA will be lower than your xwOBA, showing you had some unfortunate circumstances. Then there was park factor, which just tells you whether a park is hitter friendly or batter friendly. Every park is different. WPA is Win Probability Added, which measures how much of the work towards a win a player did. As it accumulates over the seasons you can see which players were “clutch”, or just did a lot of the work. It’s not perfect because some great players may not alter the chances of a win and thus go unaccounted for. Last but not least is WAR, Wins Above Replacement. Everyone has their own secret sauce for this one, but it’s about how many more (or less) wins a player contributes than a AAAer would. The King of WAR is Babe Ruth, who has like half of the top 10 seasonal WARs of all time. It’s a good stat to review for the “one number” that summarizes a player. The book ends with a quiz. I think I got a C.