There was a discussion at Tom Tango’s blog which led to an interesting question: what’s the difference in effectiveness between a replacement-level starter and a replacement-level reliever? To answer this, you’d need to have some way of estimating replacement level for starters and relievers. Since I have a database with boxscore data since 2010, I… Continue reading Comparing Replacement Level for Starters and Relievers
Category: Sabermetrics
More on Instant Running HOF Playing
Yesterday I talked about simulating Tom Tango’s idea for doing instant runoff voting for the Hall of Fame, and using prior year public ballot data to simulate his ranked ballots. In the comments in his blog, he asked about assuming all voters draw the line at 4 players (so long as they have at least… Continue reading More on Instant Running HOF Playing
Instant Runoff Voting for HOF
Tom Tango proposed a form of instant runoff voting for the Hall of Fame: he suggested that voters be forced to rank 10 players on their ballot, but add a line at the point where they thought players deserved induction. So if I think only 3 players get my vote (under the old system), I’d… Continue reading Instant Runoff Voting for HOF
New Categories: Pitches, Strikes, and Strike %
I’ve added three new pitching categories to RotoValue: pitches, strikes, and strike percentage. These can be used as scoring categories for a league or display-only categories, either for a whole league by administrator configuration, or by owners with RotoValue Analyst, who can add these values to the leagues where they want to see them. Here‘s… Continue reading New Categories: Pitches, Strikes, and Strike %
Rookie Raises
Mike Trout had his contract renewed by the Angels for $510,000, apparently just $20k over the MLB minimum. Trout was the American League’s rookie of the year, and according to many, he should have also been its most valuable player, an award that went to Miguel Cabrera instead. I was wondering what the highest contract for… Continue reading Rookie Raises
Playing-time Neutral Projection Comparison
In response to Jared Cross’s suggestion, I’ve done one more set of RotoValue comparisons of projection systems. This time, I’m taking players’ actual 2012 AB or IP, and scaling the projections from each system to match that level of playing time. Also, since commenters Rudy Gamble and mcbrown were asking for ZiPS data, I’ve included that… Continue reading Playing-time Neutral Projection Comparison
Reviewing Pitching Projections
Last week I posted some statistical results comparing five 2012 projections systems’ statistics to actual 2012 numbers for wOBA, a good summary offensive rate statistic. Now I’d like to run a similar analysis, but using pitching data, and 3 pitching rate statistics. I’ll be running numbers for a total of 7 systems: CAIRO – from S B… Continue reading Reviewing Pitching Projections
Testing New Projection Models, and More Offensive Stats
As I’ve noted before, my older projection model has not done as well at projecting batters’ weighted on base average (wOBA) as several other publicly available systems, so I’ve been trying a different method. I’ve now tried doing something very similar to Tom Tango’s Marcel method – take a weighted average of recent performance, regress… Continue reading Testing New Projection Models, and More Offensive Stats
Reviewing Five 2012 MLB Projection Systems
Last year I ran some comparisons of a few projection systems for major league baseball, comparing several projection systems against 2011 data. So I thought it would be interesting to run a similar comparison of the five sets of projections I made visible for 2012 on RotoValue: CAIRO – from S B of the Replacement Level Yankees Weblog.… Continue reading Reviewing Five 2012 MLB Projection Systems
More "Make-it, take-it" Data
At the suggestion of Tom Tango, I ran my basketball simulator with many different inputs to see what impact different scoring levels and game lengths might have on the change to a “make-it, take-it” rule for basketball replacing alternating possessions after scoring. The short summary is that I find overtime games are much less likely… Continue reading More "Make-it, take-it" Data