NOTE: In order for this article to make much sense, I strongly suggest reading my previous article, which covers the math and creation of Player Run Value (PRV).
Since my last article was somewhat theoretical, in its creation of Player Run Value, I thought it important to get more tangible and describe how PRV could be used to determine a player’s offensive value. The first thing to note is that PRV is not in terms of Plate Appearances, so we would expect players that play more often to have higher PRVs than those that play less. PRV assess a player’s total expected offensive contribution to their team, based on their batting statistics, namely walks, hit by pitch’s, and the various kinds of hits. For those with a deeper sabermetric knowledge, this statistic works like Wins Above Replacement (WAR) which tracks the total wins contribution of a player, not their contribution over a playing-time metric, like Plate Appearances, Innings Pitched ect. The league average PRV for non-pitchers in 2016 looks like this;
Continue reading “Building a Better wOBA Part II: Applying Player Run Value”
Since my last article, which covered the slightly superior predictive power of OPS over wOBA on runs, I began looking into reasons for this. Ostensibly, wOBA should weigh each batting outcome that results in a player reaching base safely, based on the likelihood of that outcome, a single, walk, home run etc., resulting in a run scored. In other words, if a single results in either an RBI on that hit or that batter coming around to score, every other time that a single is hit, then the weight of a single would be .5 (50% chance of a run scoring * 1 run scoring). In more sabermetric terms this is known as the change in Run Expectancy, which analyses the likelihood of a run scoring before the event occurring, then the likelihood after the event occurring. Despite this more refined method of weighing a player’s overall offensive value, if fails to do so at a greater statistical significance than OPS. For this reason I set out to build a better wOBA-like statistic.
Continue reading “Building a Better wOBA: Introducing PRV”
To the casual baseball fan the proliferation of sabermetric data and statistics in the sport may seem overwhelming and difficult to grasp; particularly with so many of these metrics getting bizarre notations like BABIP, wRAA, wRC+, and so on. These metrics deviate from the metrics most of us used growing up with the game, things like HR, BB, AVG, and RBI, both in terms of what they measure as well as their tangible-ness to the average fan watching the game. When Andrew McCutchen hits a single, most people understand that that means his Batting Average will increase by some amount. But as to what that hit meant in terms of his wRC+ or his WAR? Even if that formula was known to the average fan, you’d nearly have to be a doctoral candidate in statistics to be capable of doing that type of math in your head.
Continue reading “In Defense of OPS”