Category Archives: Strategy
Frustrated with myself for taking a chance on Jedd Gyorko and his high K% and monster SwStr% from last season, I have pondered whether or not this was a bad pick or just bad luck. Of course Gyorko’s .192 BABIP is playing some role in his struggles and that is largely unlucky, but how much is that bad strikeout rate to blame? Quantitatively, I cannot say to what degree the strikeout rate is to blame, but we can look at recent data to determine how players with high strikeout rates produce in the long term. Read the rest of this entry
A friend of mine is the commissioner of a 10-team H2H keeper league that had four teams leave the league this year. He asked me if I would take over one of the teams, so I said sure. All of the players on the four vacated teams were put into a pool for the new teams to draft their keepers from. The player pool was not that bad, probably slightly below average, and it did contain Miguel Cabrera and Mike Trout. I, unfortunately, got the fourth pick in the draft and didn’t have a chance at either of the top two guys.
In the near future I will post a more detailed outline of my ranking method, but here is the abridged version.
Step 1: Analyze player profiles to come up with projections for the following stats:
For Hitters: PA, HBP, SF, SB, BB%, K%, LD%, GB%, FB%, IFFB%, HR/FB%, IFH%, BABIP.
This results in a projection for AB, HR, BA, OBP.
For Pitchers: IP, W, SV, HBP, K%, BB%, LD% GB%, BABIP, LOB%, HR/FB%.
This results in a projection for SO, ERA, WHIP, K/9, BB/9, HR/9.
Step 2: Look at team depth charts to estimate where players will be hitting in lineups and adjust R and RBI projections accordingly. Admittedly, these are somewhat arbitrary projections, but I do use research published on Razzball to help me estimate the effect of lineup spot on R and RBI production.
Step 3: Once I have a projection, I use ESPN’s average stats per roto point data to determine how much of each stat a team needs to move up one roto point in the average league. This is how I weight each stat to come up with an aggregate score for each player. Other people use methods based on standard deviations, but when tested against my method, both methods yield extremely similar values.
Step 4: Now I adjust for positional scarcity and combine my positional rankings into an overall ranking. These are my rankings based on my projected values.
Step 5: Finally, I will subjectively move guys up or down slightly if their projected values are somewhat close or if I trust one projection over another. I may also factor in replacement value if I expect that a player will make a trip to the DL.
To come up with my projections I look at statistics like SwStr%, F-Strike%, other plate discipline statistics and PITCHf/x data to help project K% an BB%. I also use metrics like park adjusted xBABIP based on my projected batted ball profiles to help project BABIP and average fly ball distance and ISO to help project HR/FB rates. I do my best to understand correlations between peripheral statistics and fantasy statistics to make the most educated projections I can. I may, however, be aggressive on some players’ projections if the peripheral data suggests a pronounced increase or decrease in value may be coming.
As mentioned in the introductory sentence, in the future I should be posting a more detailed version of how I come up with all of my projections.