Let’s now follow up yesterday’s 2017 BABIP decliners list by looking back at who the new xBABIP would have convinced us to avoid heading into the 2016 season. Like I did when validating xBABIP using the surgers, I’ll compare how the would-have-been 2016 list performed versus their 2015 xBABIP and 2016 Steamer projections.
Last Thursday, I used my new xBABIP equation to identify 10 fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks, suggesting serious BABIP upside in 2017. Today, I’ll make many of you sad with a list of names who are at risk of major BABIP regression this season, if they don’t improve their underlying skills by a massive degree. By no means do you want to avoid these names, you just simply don’t want to value them assuming their 2016 BABIP marks are actually sustainable. But since someone in your league probably does believe the 2016 BABIP is real, you probably won’t end up rostering them at a fair price.
A week ago, I introduced the newest version of our ever evolving xBABIP equation, this time incorporating the much-needed shift data. Last Thursday, I identified the 10 fantasy relevant hitters with the greatest BABIP upside in 2017, given the gap between their 2016 BABIP and xBABIP. In the comments, I was asked if I could perform a retrospective analysis to see how the new equation would have done if I ran it heading into the 2016 season.
My Tout Wars prep continues. I have already examined the league’s historical aspects which I have used to create initial auction values and a draft outline. With the initial projections out of the way, I am refining them. One step in this process is to find the replacement level player and adjust players who will miss time accordingly.
The concept behind the replacement level player is fairly simple. If a good player is expected to miss significant time, his fantasy value is based on just the games he is expected to play. For the games he misses, some lesser player (replacement level player) will fill. The better player’s total value will be both his and the replacement player’s contribution.
For example, I don’t expect Yoan Moncada to get called any earlier than the Super Two deadline around June 1st. For the months he’s in the minors, a less talent replacement level player will be subbing in for him. The same idea works with pitchers. Tyson Ross is expected to miss at least a couple of months so a replacement is needed until he gets healthy.
Finally, after unmasking the newest version of xBABIP that accounts for shifts, it’s time to get to the names…you know, the kind of list you could actually use for your fantasy leagues this year! So let’s identify and discuss the fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks. These ten hitters should enjoy a BABIP rebound in 2017, assuming their BABIP-related skills remain stable.
It’s xBABIP week and on Monday, I unveiled the latest incarnation of my equation, this time incorporating shift data. Then yesterday, I analyzed leaguewide shift data trends and unearthed some interesting tidbits. Today, it’s finally time to talk some names. We’ll begin by looking at the players that enjoyed the biggest gains using the new xBABIP equation versus Alex Chamberlain’s 2015 version that I had been using as my primary go-to, and also the biggest losers.
So far in my Tout Wars preparation series, I’ve documented the league’s draft tendencies and the stats needed to win. Today, I’ll create the framework for player pricing. Along the way, I will show how there is no position scarcity except with catcher. At least for this league
Completing this step brings the preparation is laborious, but necessary. Once it’s done, I can spend most of my time evaluating players and their projected playing time.
For evaluating players, I utilize the Standings Gain Points (SGP) method. I previously outlined the procedure and it‘s the same method Larry Schechter recommends in his book, Winning Fantasy Baseball. Normally, this procedure is fairly straight forward since I’ve historically used three-year average values. Last year’s offensive explosion complicates the math. With more offense available, home runs, Runs, and RBIs become less important. Predicting 2017’s run scoring environment is impossible so I won’t for now. I feel I need to use a weighted average system with 2016 getting the most weight but I am just not sure how much to weight them. To get the process started, I will use the average standings from 2014 to 2016 for this work.
On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park, and then yesterday, I shared a list of six batters with significant HR/FB rate upside for 2017, given the gap between their 2016 xHR/FB rate and actual HR/FB rate. Today I’ll discuss 10 hitters with major downside.
On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park. Let’s finally get to the part you have been eagerly awaiting — the potential 2017 home run surgers. These are the guys whose xHR/FB rates far exceeded their actual HR/FB rates.
Yesterday, I introduced you to the new Statcast fueled xHR/FB rate equation I developed during my recent xMetric frenzy. It’s simple to use, requiring just a couple of variables, and its components are easily accessible. So now armed with all the data that led to the equation, let’s dive in, explore, and have a little fun.
