Effects of Weather in Fantasy Football

It seems intuitive to believe that weather plays a huge role in the fantasy performance of different positions in the NFL. When we see a quarterback struggle mightily in a snowstorm, it is easy to jump to the conclusion that weather has a significant impact on the fantasy performance of players. When deciding the players to start for a particular week, weather seems to be a reasonable variable to take into account. Another variable to take into consideration when making such decisions is the opponents the players are facing. Is weather as significant to this decision as opponent defenses? The focus here is on quarterbacks and running backs, though kickers presumably could be strongly affected by weather as teams rarely attempt field goals or even kicks for the extra point in extreme weather conditions.

An Update on Positional Adjustment

Positional Adjustment has always been a point of contention about WAR. While most understand the principle of positional adjustment, I doubt that anyone has really scrutinized the process behind the values for positional adjustment. The established values for positional adjustment were developed by Tom Tango using UZR data for players who switch positions over multiple years. He took some liberty with the numbers, and adjusted the values based on relation to offensive value and his own intuition. I have always wondered why so few people questioned these values and accepted them as they are, so I decided to verify these values on a slightly different methodology.

NBA Finals Prediction

Spurs in 6

NBA Playoff Conference Finals Prediction

Heat in 6

Spurs in 7

NBA Playoffs Second Round Prediction

Pacers in 6
Heat in 5

Spurs in 6
Clippers in 7

NBA Playoffs First Round Prediction

Pacers in 5
Heat in 5
Nets in 6
Bulls in 6

Spurs in 5
Thunder in 6
Clippers in 6
Rockets in 7

History of Pitchers as Position Players

The various projection systems are the closest we can come to predicting future. I was thinking of what they currently lack, and the first thing that came to mind was pitchers as batters. I then checked how each team did with their pitchers last season. It turns out that the spread from the best team, the Dodgers, to the worst team, the Pirates, is less than three wins. The true talent level is much narrower than that, and there does not seem to be much advantage gained by including pitcher batting in projections. Instead, I decided to look at the history of pitchers as position players.

Brett Gardner and Positional Adjustment: CF vs COF

Brett Gardner is the typical center fielder, with speed and range in the field. The New York Yankees just signed him for a four-year extension of 52 million dollars, but to play left field alongside Jacoby Ellsbury instead of center field. There are concerns that Gardner’s bat may not play in a corner outfield spot, that his value would be lower at LF than at CF. This is the effect of the positional adjustment. As a player’s fielding contribution is compared to other players of the same position, we have to adjust our evaluation of a player based on where he plays in the field. The established positional adjustment has a CF getting a boost of +2.5 runs over a full season while a LF or RF gets a penalty of -7.5 runs. In theory, a CF moving to LF would gain 10 runs in the field to make up the difference, as they are now compared to worse fielders. I will be testing whether this statement holds true in reality.

Predicting LOB%

In my article last week, I developed xLOB% as a descriptive statistic to estimate a pitcher’s LOB%. In this article, I will attempt to predict LOB% of a pitcher using his statistics from the previous season. Despite its fairly weak predictive results, pLOB% explains 12.7% of the variation in a pitcher’s LOB% in the following season, better than Steamer’s projection and kLOB%.

Estimating LOB%

Luck has been the explanation whenever a pitcher has a significantly lower ERA than his FIP. There are two statistics where luck plays a huge role, BABIP and LOB%. Using Steve Staude’s pitching stat correlation tool, we can see that BABIP only has a correlation of 0.156 from one season to the next, while LOB% has a correlation of 0.205, for pitchers with a minimum of 30 innings pitched from 2007 to 2013. These numbers are much lower than the correlation of K% or BB%, suggesting that a large portion of BABIP and LOB% are subject to random variation and independent of a pitcher’s skill. However, the correlation is not 0. They are not completely random, and a pitcher can still play a small role in controlling their BABIP and LOB%. Many writers, including Steve, have tackled the issue of BABIP using batted ball data. In this article, I will be estimating a pitcher’s LOB% for the current season. This is not supposed to be a predictive stat, but a descriptive one. Think of it as FIP. While FIP estimates the pitcher’s ERA using strikeouts, walks and homeruns, xLOB% estimates the pitcher’s LOB% given his other pitching statistics for the same season. I will be introducing pLOB% in the next article, which attempts to project LOB% of a pitcher for the following season.

Random Thoughts from Georgetown-Marquette

(Small sample size caveat: This is the second time I have watched a Georgetown game this season)

Let me run through the last minute of regulation, with Georgetown up by four with the ball: