Players of the Week: May 20-26 2013

RotoValue makes it easy to see player statistics for any date range, so today I’m going to link to recaps of the week for three different league configurations. First, 5×5 Mixed: MVP: Mike Trout. Trout hit .462 with 2 HR, 10 runs scored, 7 RBI, and 4 SB. Trout had a relatively slow start (.261… Continue reading Players of the Week: May 20-26 2013

Auction Weekend Recap

This past weekend we had the auctions for my two long-time leagues, the Park Slope Rotisserie League, a traditional 4×4 AL-only league, and the Ezra Stiles Rotisserie Association, a 4×4 NL league. This is the 25th season of the ESRA NL, of which I was a founding member, and the 26th year of the Park… Continue reading Auction Weekend Recap

Going 9 AL Recap

So I did a draft for an AL league sponsored by Going 9 Baseball, with 12 teams, so it’s a deep league. We drafted 25 players each, and it took a little over 3 hours to complete online. After doing a mock draft in a shallow mixed league a few weeks earlier, I had hoped to… Continue reading Going 9 AL Recap

Projected Playing Time Comparison

My last post effectively compared projection systems by how well they predicted player skill level: I scaled each projection to match 2012 actual ab/ip, but used the rates each system projected to generate stats. Tango asked if I could run the reverse, a comparison where I used prorated actual 2012 stats but kept only the playing… Continue reading Projected Playing Time Comparison

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

Revised Projected RotoValue Comparison

Tom Tango highlighted my previous post on comparing computed RotoValue prices from projection systems, and he and others in the comments had some good suggestions for improving the player pool. So I’ve run some more data with slightly different sets of players. First, for each league configuration, I’m simply using the top 230/240 players in the… Continue reading Revised Projected RotoValue Comparison