Can’t count due to data or would have put them in – errors / passed balls.
Most of the stuff I’m counting adds goes into OPS, so I did a quick check for normality of distribution and grabbed the standard deviation to assign fantasy cost to each player based on a 1-5 scale (same scale Tom used, but I don’t know how he assigned the numbers to begin with).From the 2014 ABL player stats, we have a mean OPS of 0.78850 and a standard deviation of 0.118. As a plan for week 1, I am just going to look at the summary stats of each player from the previous season (ABL where I’ve got ABL, otherwise whatever league they last played in regardless of level. Given the range of steadily increasing skill in the ABL, I’m not too worried if the first couple of weeks are a bit blippy. Fantasy cost of player based on:
-2sd (eg, OPS .5519 or less) = costs 1 point
-1sd (eg .5519 <OPS <0.6702) = costs 2 points
-1sd to +1sd (eg .6702 <OPS<.9068) = costs 3 points
+1sd to +2sd (eg. 9068 <OPS<1.0251) = costs 4 points
+2sd (eg OPS 1.0251 or more) = costs 5 points.
Yes this is going to be an insane amount of work, especially since at this point, it looks like I’m going to have to dig through club news sites to get a sense of who is actually playing, since the rosters aren’t up yet. And if it’s anything like last year, they won’t be up until just before the season starts.
Fantasy costs will of course be reviewed after the first week, and will be reviewed throughout the season, but I’m hoping for a bunch of quite diverse batters worth 3 points that will reward someone who’s paying closer attention to the guys who aren’t putting up superstar slugger numbers, but doing good stuff regardless.
I’ll be running a fairly basic fantasy points / fantasy cost to try and keep the handicapping reasonable.
|Each full inning pitched||2|
I’m punishing hitting batters because you’re either pitching wild and I don’t have another way to measure this, or you’re hitting people deliberately, which is gross.
Strikeouts per innings pitched is another pretty normal distribution.
And just to reiterate, I’m trying to use stats that are largely in the pitcher’s control (or lack thereof), thus the dependence on components of FIP, rather than using wins/losses/saves or even hits.
Season level fantasy scores comparison
Comparing a season of best strikeout pitcher fantasy scores vs best OPS hitting using these numbers gets us:
Best 2014 pitcher by strikeout count (Ryan Searle) – fantasy score = 223
(Best 2014 fantasy score Craig Anderson with 284)
Best 2014 batter by OPS (Aaron Miller) – fantasy score = 193
(Best 2014 fantasy score Brandon Dixon with 203)
I’m actually quite happy to leave it with pitching having the big variable fantasy scores, even at the fantasy score per game level you’re looking at quartile scores of -11, 5, 10, 15, 21 for pitching versus 2, 2.7, 3.1, 3.7, 5.4 for batters. Partly this is because you’re only going to see your starting pitcher once per week and therefore a good game for them does make this much difference to a fantasy team. And also because if a pitcher fails badly, a tonne of points go the other way and I think it’s nice to reflect that a little.
Also, depending on the length of the series (whether it is 3 games or 4) the series mean average works out pretty close to the same for pitchers and fielders.
In terms of how to define points, I’ll probably go with K/9 to walk ratio and check normal distribution and work off standard deviations as per the batters. But it’s surprisingly nearly midnight and I’m going to bed!
 Also, you may notice that I called it OBPSL rather than OPS. That’s because ‘Ops’ is a command in R, so every time I try to do something with OPS, I get all kinds of weird failures because I’m not giving the Ops command the things it wants to do stuff with. Also, I don’t know what Ops does yet, but I’m sure it’s impressive.