Comments on: How Good Is That Projection? http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/ Daily baseball statistical analysis and commentary Sun, 12 Feb 2012 21:26:01 +0000 hourly 1 http://wordpress.org/?v=3.3.1 By: Brian Cartwright http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-68989 Brian Cartwright Fri, 27 Mar 2009 18:33:32 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-68989 Oliver projections for previous seasons have not been posted on the Internet, but I can run them for all the players currently in my database, generally to 1998. However, the further back, there are a decreasing percentage of the players represented. Soon (weeks) I will have a complete set of minor league stats from 2005-2008 (GameDay data), and will add that to complete major league records 1998-2008, with scattered records for both before those dates. I will be glad to send you an email with projections for previous seasons. Oliver projections for previous seasons have not been posted on the Internet, but I can run them for all the players currently in my database, generally to 1998. However, the further back, there are a decreasing percentage of the players represented. Soon (weeks) I will have a complete set of minor league stats from 2005-2008 (GameDay data), and will add that to complete major league records 1998-2008, with scattered records for both before those dates.

I will be glad to send you an email with projections for previous seasons.

]]> By: Ben http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-68978 Ben Fri, 27 Mar 2009 18:07:21 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-68978 Are Oliver projections available for previous seasons? If so, where can I find these? Are Oliver projections available for previous seasons? If so, where can I find these?

]]> By: Peter Jensen http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-66811 Peter Jensen Wed, 18 Mar 2009 05:52:34 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-66811 Brian - Perhaps I have misunderstood what are you are doing. Could you explain again what exactly you are comparing in chart 2 and how it differs from chart 3? Brian – Perhaps I have misunderstood what are you are doing. Could you explain again what exactly you are comparing in chart 2 and how it differs from chart 3?

]]> By: Brian Cartwright http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-66742 Brian Cartwright Tue, 17 Mar 2009 22:44:08 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-66742 What I did not explicitly state was that in the first two charts, when comparing two rates, the sample size is the lower of the two. So it was 350 or more PA in year 1 compared to 325-375 PA in year 2, then 400 or more PA in year 1 compared to 375-425 PA in year 2, etc. This is standard procedure to express the results by the lower of the two sample sizes. General matched pairs methodology does it the same way, the counting stats of the larger sample are scaled down to the size of the smaller sample, or to the harmonic mean of the two sample sizes, which will not be too much larger than the smaller. This is because the level of unreliability of the smaller of the two samples will determine the outcome. I was saving comparing projections to a later article, but I could have shown that ZiPS and PECOTA gave just about the same results in chart 3. For the various subgroup tests that I have run, the rms error is always just about .030 wOBA, regardless of which of the three projections. Therefor, I've concentrated on total error, looking for any biases. What I did not explicitly state was that in the first two charts, when comparing two rates, the sample size is the lower of the two. So it was 350 or more PA in year 1 compared to 325-375 PA in year 2, then 400 or more PA in year 1 compared to 375-425 PA in year 2, etc. This is standard procedure to express the results by the lower of the two sample sizes. General matched pairs methodology does it the same way, the counting stats of the larger sample are scaled down to the size of the smaller sample, or to the harmonic mean of the two sample sizes, which will not be too much larger than the smaller. This is because the level of unreliability of the smaller of the two samples will determine the outcome. I was saving comparing projections to a later article, but I could have shown that ZiPS and PECOTA gave just about the same results in chart 3. For the various subgroup tests that I have run, the rms error is always just about .030 wOBA, regardless of which of the three projections. Therefor, I’ve concentrated on total error, looking for any biases.

]]> By: Peter Jensen http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-66666 Peter Jensen Tue, 17 Mar 2009 19:07:29 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-66666 <i>What we have here is an equation x - y = z. We have worked to reduce the mean error of x in order to reduce z - but y is still the same. The result of a computation is only as accurate as its least accurate input. No matter how perfect any one projection system is, as long as it’s mean error is less than that of comparing any two consecutive seasons, that increased accuracy will be masked by the noise inherent in a single season measurement.</i> This statement seems ridiculous to me. If you have x - y = z where x is year 1 performance and y is year 2 performance and you know the error range for a single years performances is +-10 then the error range for z is going to be +-20. If you instead use multiple years, and adjust for aging to try and more closely have x represent true talent and you are successful so that your error range of x is decreased to +-5, then the error range of z is going to be +-15. The actual mean error will depend on the distribution curves for each equation, but you should end up with a mean error of the 2nd example being about 75% of the first. That you did not is not a result of "the increased accuracy being masked by the noise inherent in a single season measurement". It is a result a result of the particular manipulations that you performed to give you your projection having failed to provide you with any better predictor than the previous year's stats. What we have here is an equation x – y = z. We have worked to reduce the mean error of x in order to reduce z – but y is still the same. The result of a computation is only as accurate as its least accurate input. No matter how perfect any one projection system is, as long as it’s mean error is less than that of comparing any two consecutive seasons, that increased accuracy will be masked by the noise inherent in a single season measurement.

This statement seems ridiculous to me. If you have x – y = z where x is year 1 performance and y is year 2 performance and you know the error range for a single years performances is +-10 then the error range for z is going to be +-20. If you instead use multiple years, and adjust for aging to try and more closely have x represent true talent and you are successful so that your error range of x is decreased to +-5, then the error range of z is going to be +-15. The actual mean error will depend on the distribution curves for each equation, but you should end up with a mean error of the 2nd example being about 75% of the first. That you did not is not a result of “the increased accuracy being masked by the noise inherent in a single season measurement”. It is a result a result of the particular manipulations that you performed to give you your projection having failed to provide you with any better predictor than the previous year’s stats.

]]> By: Brian Cartwright http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-66612 Brian Cartwright Tue, 17 Mar 2009 16:05:38 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-66612 Reminds me of one of the points I need to lead the comparative article with - Oliver's wOBAs are park adjusted, representing the player in a neutral park. Each batting component (SI, DO, TR, HR, BB, SO) is normalized by ballpark, league and age, then reassembled into a batting line, then finally calculate the new rate stats, including wOBA. To the best of my knowledge, ZiPS and CHONE are adjusted to the player's home park at the time the projection was made. Marcel does not park adjust. Oliver will have lower wOBAs than ZiPS or CHONE for players in Colorado, Milwaukee, Philadelphia, Cincinnatti, etc, but I am also working on a new formula for HR factors which will not dock the premier power hitters as much, but the medium and lower guys more so, when they plau in a small park. Reminds me of one of the points I need to lead the comparative article with – Oliver’s wOBAs are park adjusted, representing the player in a neutral park. Each batting component (SI, DO, TR, HR, BB, SO) is normalized by ballpark, league and age, then reassembled into a batting line, then finally calculate the new rate stats, including wOBA. To the best of my knowledge, ZiPS and CHONE are adjusted to the player’s home park at the time the projection was made. Marcel does not park adjust. Oliver will have lower wOBAs than ZiPS or CHONE for players in Colorado, Milwaukee, Philadelphia, Cincinnatti, etc, but I am also working on a new formula for HR factors which will not dock the premier power hitters as much, but the medium and lower guys more so, when they plau in a small park.

]]> By: bikozu http://www.fangraphs.com/blogs/index.php/how-good-is-that-projection/#comment-66566 bikozu Tue, 17 Mar 2009 08:51:42 +0000 http://www.fangraphs.com/blogs/?p=3570#comment-66566 Cool. Some good stuff in here that I haven't read/seen previously. That factor of .7 thing is pretty interesting too. When will we get park factored wOBA (the one used on this site that includes baserunning etc) on the projection page? This information could be useful. Cool. Some good stuff in here that I haven’t read/seen previously. That factor of .7 thing is pretty interesting too.

When will we get park factored wOBA (the one used on this site that includes baserunning etc) on the projection page? This information could be useful.

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