# MODERATORS

## Saturday, March 09, 2013

Baseball Prospectus created another statistics called the peripheral ERA. This measure of a pitcher's performance takes hits, walks, home runs allowed, and strikeouts while adjusting for ballpark factors. Batting average on balls in play BABIP is another useful measurement for determining pitcher's performance.

Others have created various means of attempting to quantify individual pitches based on characteristics of the pitch, as opposed to runs earned or balls hit. Value over replacement player VORP is considered a popular sabermetric statistic. This statistic demonstrates how much a player contributes to his team in comparison to a fake replacement player that performs below average. Wins above replacement WAR is another popular sabermetric statistic that will evaluate a player's contributions to his team.

Many traditional and modern statistics, such as ERA and Wins Shared, don't give a full understanding of what is taking place on the field. Structured quantitative analysis is capable of explaining many aspects of the game, for example, to examine how often a team should attempt to steal. Related rates can be used in baseball to give exact calculations of different plays in a game. For example, if a runner is being sent home from third, related rates can be used to show if a throw from the outfield would have been on time or if it was correctly cut off before the plate.

Momentum and force is a similar application of calculus in baseball. Particularly, the average force on a bat while hitting a ball can be calculated by combining different concepts within applied calculus.

First, the change in the ball's Momentum by the external force F t must be calculated. The momentum can be found by multiplying the mass and velocity. The external force F t is a continuous function of time. Sabermetrics can be used for multiple purposes, but the most common are evaluating past performance and predicting future performance to determine a player's contributions to his team. Most baseball players tend to play a few years in the minor leagues before they are called up to the major league.

The competitive differences coupled with ballpark effects make the exact comparison of a player's statistics a problem. Sabermetricians have been able to clear this problem by adjusting the player's minor league statistics, also known as the Minor-League Equivalency MLE.

To compare key performances among certain specific players under realistic data conditions. The evaluation of past performance of a player enables an analytic overview. The comparison of this data between players can help one understand key points such as their market values.

In that way, the role and the salary that should be given to that player can be defined. To provide prediction of future performance of a given player or a team. When past data is available about the performance of a team or a specific player, Sabermetrics can be used to predict the average future performances for the next season. Thus, a prediction can be made with a certain probability about the number of wins and loses.

To provide a useful function of the player's contributions to his team. Given that correlation, we can sign or release players with certain characteristics. A machine learning model can be built using data sets available at sources such as baseball-reference. This model will give probability estimates for the outcome of specific games or the performance of particular players. These estimates are increasingly accurate when applied to a large number of events over a long term.

Predictions can be made using a logistic regression model with explanatory variables including:. Many sabermetricians are still working hard to contribute to the field through creating new measures and asking new questions.

Bill James' two Historical Baseball Abstract editions and Win Shares book have continued to advance the field of sabermetrics, 25 years after he helped start the movement. This acronym stands for Player Empirical Comparison and Optimization Test Algorithm , [28] and is a sabermetric system for forecasting Major League Baseball player performance.

This system has been owned by Baseball Prospectus since and helps the website's authors invent or improve widely relied upon sabermetric measures and techniques. Beginning in the baseball season, the MLB started looking at technology to record detailed information regarding each pitch that is thrown in a game.

The website also specializes in publishing advanced baseball statistics as well as graphics that evaluate and track the performance of players and teams. From Wikipedia, the free encyclopedia. The Art of Winning an Unfair Game. A Journal of Baseball History and Culture. Archived from the original on Baseball, Statistics, and the Role of Chance in the Game.

Think Tank with Ben Wattenberg. Retrieved November 2, The Wall Street Journal. Retrieved 24 October Putting the Science of Baseball Statistics to work. Triumph and Tragedy in Mudville: A Lifelong Passion for Baseball. Retrieved 30 August Just tuned into Cards-Giants game, must say I'm a little disappointed Bochy decided to sit half his starters on one day.

I know Torres had some issues, but to give Posey, Burrell, and Tejada the day off as well. They had an off day on Thursday so they should have been decently rested. I'm a Giants fan btw I'm pretty new to handicapping and I haven't pay this close attention to lineups in the past, but seeing all these starters out I don't like it. I will be running numbers for tomorrow's games and narrowing my plays to only the best ones.

Should have picks up in hours. Good job on your picks. Also, the season is young and still working on improving my model, so tread carefully. Top picks in bold. All plays are for 1 unit. Last edited by 11 d. Why did you decide to look at pitchers' FIP, because that stat is great for ranking pitchers but I don't think leaving out a team's defense entirely is the right idea. BTP Week 1 pts.

I'll look for other ways, and any suggestions are welcome. Still not sure how I would work it into my model and what kind of weight it deserves compared to hitting and pitching. I'll continue thinking about it and would appreciate any input. Still working on system, I did add defense, but realized I way overstated it, sticking with earlier picks for today. Hopefully, this makes the system more accurate. Felix gives up 7 and Sea comes back from down 8 with 5 outs left using 3 runs off walks.

Toronto's bullpen prior to this game wasn't bad either. I feel pretty good about these picks. The system now incorporates my interpretation of the most important sabermetric statistics for hitting, pitching, and defense, wRAA, xFIP, and DRS, respectively, using data for projected starting pitchers and data for everything else.

Sample size is still a little small for , but will improve with a few more weeks of games. All 1 unit, pick of day bold. All times are GMT