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Who would you rather have down the stretch, Oswalt, Greinke, Garza, Dempster, assorted others or Tilman? Now that Machado has shown signs of being a solid major leaguer, was Headly really worth Jake, Matusz and a top position prospect? The fact that we are competing without giving away any top prospects bodes well for the future. Maybe DD is lucky, maybe he's a mad genius, whatever. I like where we are right now and I like the direction DD is taking this franchise.
WARNING: Major tl;dr potential here. If that's okay with you, please keep reading. About two weeks ago, I made the following post here: While I'm proud of that post, and a few people evidently liked it enough to rep me for it, I think most people on this board are beyond the point where they consider batting average to be a worthwhile stat for player evaluation. That's a good thing, because as I tried to demonstrate above, batting average is next to worthless, especially for predictive purposes. However, I regularly see people on OH (for example, this Frobby thread) using OPS as essentially the new standard offensive stat, replacing what BA was for the longest time. And honestly, I'm not really okay with that. Why? Well, first I'll copy the example from my post above. ................AB.....H......2B....3B.....HR..... BB......BA....OBP...SLG Player A....560....190....22.....1.....10......40.....339 ...383....436 Player B....500....130....17......1....23......100....260 ...383....436 If you're using OPS, Players A and B are equal. While that's better than what BA would suggest, that Player A is significantly better than Player B, it's still not true. A team of nine Player Bs would score an estimated fifty runs more than a team of Player As. So OPS is clearly not the best evaluator of value. The same argument about the predictive qualities applies. I'm not going to rehash it here beyond that the players the closest to the Player B line since 1969 are much better than the players closest to Player A, therefore OPS is not a particularly good predictive stat either. Sidebar: I can't figure out anywhere else to work this into this post, but I feel it needs to be said. If you insist on using some form of OPS, please at least use the very easy league- and park-adjusted version, OPS+. Len Gabrielson's OPS in 1968 was .765. Neifi Perez's OPS in 2001 was .771. But there's absolutely no way Neifi was the better hitter, because he played in the most hitter-friendly environment ever, 2001 Coors Field, and Gabrielson played in the least hitter-friendly environment ever, 1968 Dodger Stadium. OPS+ adjusts raw OPS numbers for the environments players played in, and sets them relative to league average, which is a 100 OPS+. So Gabrielson's OPS+ was a 137 (very good) and Perez's was 81 (bad). But what should we use instead of OPS? Before I answer that question, I'd like to look at why OPS is kind of an okay measure of offensive performance. For every (AB+PA), OPS applies the following values to each potential outcome: BB/HBP: 1 1B: 2 2B: 3 3B: 4 HR: 5 The simplest way to figure this out is to actually calculate the values. If a player walks, his OBP for that plate appearance is 1.000 and his SLG is .000, for an OPS of 1.000. If he singles, that SLG becomes 1.000, and the OPS is 2.000. And so on and so forth. EDIT (thanks skanar): These values only hold true for the first successful PA following any number of outs. Beyond that, the values can vary a bit. I apologise for my flawed math. However, who decided a HR was five times as valuable as a walk? Or that a single was twice as valuable as a walk, and a HR was 2.5 times more valuable than a single? Does that sound right at all? Well, it shouldn't. As a batter, there are two aspects of your performance that can be measured: getting on base and moving runners over. OPS kind of does this, but crudely. Here's where things get mathy. Based on empirical PBP data, people smarter than me created linear weights. The way it works, as best as I can gather, is as follows: First, we have the potential of a runner on a certain base with a certain number of outs to score. A triple with two outs has less value than a triple with no outs in terms of scoring runs, because it is less likely that a player will score from third with two outs. We then compute the potential values for each potential situation. This number is the getting on base value. Second, we have the amount an outcome at the plate affects the potential of runners on base to score. We again compute the values for each potential situation based on empirical PBP data. Next we multiply by the frequency of each event occurring, because while a HR can sometimes be worth four runs, it is much less common than one, two, or three. To reflect reality, we need to not assume each case has an equal possibility of occurrence. After multiplying the "getting on" and "moving up" values of each event in each possible situation, we now have an empirical value for how much any outcome at the plate affects run scoring in any given situation. Tom Tango has compiled a chart of these based on a 1999-2002 run environment here. The important thing in that chart is the bottom line, the average value for each event. It's not perfect, because the value of an event will always be dependent on context, but for a context-neutral large-scale evaluation (i.e. comparing players), it is as good an approximation as we can get. It's a whole lot better than the values assigned by OPS, that's for sure. So now that we've assigned these values based on outcomes from years and years of actual baseball being played at the major league level, how do we actually work them into a stat that we can easily access? Luckily, that's already been done for us. Fangraphs calls it weighted On-Base Average, or wOBA. It's scaled to league average OBP, so the league average OBP will be the same as the league average wOBA (usually in the .320-.335 range), so, like OPS+, it's relative to league, and not just raw numbers with no context. Fangraphs uses wOBA as the basis for their offensive stats, like their estimated batting runs, which are a major component in Wins Above Replacement (WAR), a statistic many here feel comfortable using. And that's perfectly fine, so long as you recognize what you're using it for and don't try to use it for something it's not meant to be used for. Honestly, though, I don't like WAR. And here's why (this is where I diverge from accepted research and start to make my own assertions): WAR measures value, not quality. The two are not interchangeable. A player is not better because he posts a higher WAR value. Apart from the general problems with fielding metrics, WAR is a very good estimator of how valuable a player was to a team. But that's all it is. First, it's a counting stat. Counting stats are inherently inferior to rate stats in evaluating quality, because they can value playing more at a lower quality over playing less at a higher quality, when the player who played at the higher quality was by definition a better player. But unlike strictly batting or fielding stats, there is no way to properly convert WAR into a rate stat. Adjusting by PA conflicts with the fielding aspect, and in an extreme case could turn a player who played only as a defensive replacement and accumulated maybe .2 WAR while only coming to the plate once into a more valuable player than someone who accumulated 8 WAR in 500 PA (.2/1 > 8/500). And adjusting by innings played would have a similar effect on players who were solely pinch hitters. So it's a counting stat, and must remain a counting stat. This is okay for value, but not okay at all for quality. Second, WAR includes a positional adjustment. In Fangraphs' explanation of the positional adjustment they use, they admit to making three assumptions that are okay to make if the goal is to measure value, but for measuring quality they cannot be made. The assumptions are: 1. Major league teams are being perfectly efficient with who they put, and where. 2. Left-handed players and right-handed players can each play every position. 3. Offensive ability is not independent of the position being played. It has been demonstrated time and time again that the same offensive production from a left fielder is less valuable than from a shortstop. But does that necessarily make the shortstop a better player? No, it doesn't, because you're assuming that the left fielder is not a good enough player to play shortstop. Positional adjustments mean that WAR is inherently dependent on the way a team uses its players, which is when it entirely leaves the realm of measuring quality. So all WAR is good for is measuring a player's value to his team. Which means it's great for stuff like deciding who should win the MVP award, but is that really what we want out of a stat? Let me use an example here. The second-best (after Cliff Lee) starting pitcher in the AL in 2008 in my opinion was Justin Duchscherer. His ERA+ was 163 and his WHIP was 0.995. But his WAR (using the B-R formula) was 3.9. John Danks also had a great year, with an ERA+ of 138 and a WHIP of 1.226. Great numbers, but I don't think anyone would argue he was a better pitcher than Duchscherer based solely on quality stats. Yet Danks' WAR was 6.4, simply because he pitched more. Yes, Danks was more valuable, but value is often inhibited by circumstances beyond the player's control, be it injury, a stubborn manager, or teams gaming the service clock. Evaluating quality is better because it removes context, thereby reaching a more pure outcome that tells us something definitive and unqualified. In short, don't use OPS, use wOBA, but don't use WAR outside of its intended purpose. Use rate stats whenever possible. Above all, recognize what each stat seeks to measure, and never try to use it to measure something it cannot measure. Batting average does not accurately measure overall offensive quality. wOBA does that, but does not accurately measure overall player quality. There is no one stat that can do that. WAR measures value, but does not measure quality. Basically, when making an argument, decide what you are arguing, select which stats best measure what you are arguing, and use them. By doing so, hopefully we can have discussions from which we can all learn, rather than simplistic throwing back and forth of numbers with little reason and no context. Download the Fangraphs search engine for Firefox here. I'm sorry this is so long.