Over the years, I've tried my hand at creating a number of stats in an attempt to help us better understand the players that drive our fantasy baseball games. Some have been quite innovative, others are merely practical, and others have fallen by the wayside, but I thought it would be useful to present them all here, in one place, for easy reference (with links to the original article introducing the stat).

The context in which a player posts his stats are key. Are they accumulated in the American League or the National League? A "pitcher's" ballpark or a "hitter's" ballpark? Against good batters or poor batters? If a pitcher faces a disproportionate number of Adam Dunn-type hitters, he is going to strike out and walk more batters and allow more home runs than we'd expect. Because a pitcher has no ability to control the batters he faces, though, we can't consider this a repeatable skill and must, therefore, neutralize a pitcher's stat line based on the opposition he faces. The same thing goes for any other number of contextual factors. The last published record of CAPS accounted for:

- Past home ballpark
- Current home ballpark
- Past road ballparks
- Current road ballparks
- Past quality of opponents (neutralized)
- League switch adjustments
- Ground balls adjusted for league average line drive rate (xGB)

What we'll see is that extreme GB pitchers have higher xBABIPs and extreme FB pitchers have lower xBABIPs (while also realizing that guys who induce a lot of pop-ups will have low xBABIPs too). In 2009, for example, GB'er Aaron Cook had a .314 xBABIP while FB'er Jered Weaver had a .291 xBABIP. — THT 1/25/10

To calculate K/BB RI, you first determine how many strikeouts, walks, and batted balls occurred per major league game. Next, multiply each event by it's corresponding relative run value, giving you runs per game. For batted balls, you need to back-calculate runs per game using total league average runs per game and runs per game on strikeouts and walks. You also back-calculate batted balls per game by subtracting strikeouts and walks per game from total batters faced per game. Divide runs per game (on batted balls) by batted balls per game to get a relative run value on batted balls.

For each individual pitcher, you then multiply his strikeout, walk, and batted ball per game figures by each event's relative run value. After doing this, you get a runs per game figure for each event. Add them up. Subtract this number from league average runs per game, and you arrive at the impact strikeouts and walks have on a pitcher's runs allowed. — THT 2/14/08

Analysts often like to credit deviation further from the mean than this to a pitcher's home park, but that simply is not the case (unless the pitcher has thrown a disproportionate number of games at home, and even if he has, that shouldn't be expected to continue going forward). Simply put, HR park factors are not quite as extreme as most seem to believe. — THT 1/25/10

xLOB% is calculated using a regression formula derived from BAA and BB%. Now, of course, BAA is subject to extreme variation since it is largely comprised of BABIP. So instead of using actual BAA, we use xBAA, which accounts for the pitcher's actual K rate (the more Ks, the fewer opportunities for hits) and his xBABIP. What we end up seeing is that good pitchers end up leaving more runners on base (2009 Tim Lincecum: 75.6 percent) while bad pitchers let more score (2009 Jeremy Sowers: 68.1 percent) than league average (2009: 71.9 percent). — THT 1/25/10

To calculate TQS, I first normalize the batted-ball figures to account for disproportionate line drives. After that, I apply relative run values to strikeouts, walks, hit-by-pitches, adjusted ground balls, adjusted fly balls, adjusted pop-ups, and adjusted line drives. I take this figure, multiply by nine, and divide by innings pitched to get a sort of makeshift, one-game expected ERA. I then apply a simple above replacement level measure, calculated as (6 - expected ERA)*IP/9. I call this the TQS score. From here, I take every TQS score for the entire year and find the standard deviation among them. These standard deviations are then used to classify each start as one of the following: great, good, above average, below Average, bad, or awful. — THT 2/20/08

To calculate tHR, every home run is run through Greg Rybarczyk's HitTracker system in 30 environments: every park in the league with average weather for that park. The homers that are given a No Doubt label are counted up and then entered into an exponential regression equation to arrive at the total number of home runs that the hitter should be expected to hit based upon. — THT 7/9/08

Since a hitter is swinging, we can assume it's because he believes he can hit the ball (yes, this isn't always true, but it's good enough for our purposes), and a ball within the strike zone is definitely capable of being hit (by focusing on in-zone pitches, we ignore the times where a batter swings and misses on pitches out of the zone. This is because these are more likely to be caused by poor judgment, not poor bat control — the batter shouldn't be swinging at a ball outside the strike zone if he isn't able to hit it).

So the percentage of times he does what he intends to do (make contact) when he should be expected to (when it's in the strike zone), I contend, gives us a good measure of bat control. — THT 9/16/08

in LABR History

2009 - NL

the MLB Scouting

Bureau Scout

Development Program

(aka Scout School)

Recognize Me From: