With prospect week happening on the main website, I’m going to look at the hitting prospects ranked by their NFBC ADP (average draft position). This will be the batter’s value just in redraft leagues, not in keeper or dynasty leagues. I’ve included the player’s average, low, and high ADP values along with their Fangraphs prospect ranking.

Ronald Acuna (133 ADP, 88 min, 207 max, #2 prospect)

As the first rookie hitter off the board, his price is just a little too high for me considering the options available after him. He’s a talented ball player who is consistently ranked as either the #1 or #2 prospect in the game. He’s going to be good but how good in 2018 is the question. I have two reservations about him.

First, the no league has had a chance to adapt to his weaknesses and then have Acuna adapt back. Even pitchers found Mike Trout’s “weakness” and he had to adapt. Acuna will need to also. The question will be when the adaptation will occur and how long will it take. Will it happen in the minors and take a couple months? Or will happen in the big leagues and only be a week or two. This adjustment time could really frustrate owners.

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Yesterday, I began my discussion of another one of my xHR/FB rate equation’s components, FB Pull%, and shared the hitters who posted above average Brls/True FB and Avg FB Dist marks, but below average FB Pull% rates, hinting at upside if a change in approach is made. Today, I’m going to check in on the hitters who posted below average Brls/True FB and Avg FB Dist marks, but above average FB Pull% rates. These hitters are at greater risk of HR/FB rate regression given their heavy reliance on pulling their flies. If that skill erodes or approach is altered, there would be major downside.

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It’s tough to create a perfectly balanced team on draft or auction day. Owners are feeling the push-and-pull of trying to balance all five categories in a roto league. Mid-draft, many owners decide to drop a category with the hope of finding the needed stats on the waiver wire. Knowing which stats can be found can be tricky. By looking back at last season’s Tout Wars leagues, a decent idea of available stats can be determined.

One feature of the OnRoto.com fantasy league website computes the league’s final standings using just the drafted teams (nine pitchers, 14 position players). I took these draft values and compared them to the actual final values for each of the four roto leagues (12-team AL and NL-only and the two 15-team mixed leagues).

Some specific notes on these leagues. First, they are deeper than most leagues so every player who might be good is already owned. As for the timing of the mixed draft (the other three were auctions) happened a few weeks before the other three. Additionally, only the 23-man rosters were used used for the projected standings. Each team had an additional five or six-person bench.

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One of the components of my new xHR/FB equation is fly ball pull percentage (FB Pull%). Sadly, I have spent significantly more time discussing barrels per true fly ball and average fly ball distance, so I’m going to change that. Let’s talk FB Pull%!

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Swing plane has always been an important metric for evaluating batters, but there has never been an objective measure of swing plane in the public. Privately, through wearable technologies or video analysis one could obtain information regarding swing path, which has since gone on to become a valuable tool for coaching and training. Even still, we lacked data from actual games.

Over the past two years many writers and researchers have turned to using Exit Velocity vs Vertical Angle charts to analyze batter performance. Ranging from Rob Arthur to David Kagan to Alan Nathan. It is a great way to visualize the data. I have wondered in the past whether you could use these information to estimate the average plane of the bat on impact. If you were to plot a second order polynomial regression on top of such a chart and take the derivative of the function you could find the peak launch angle, that is the angle with the highest average exit velocity. Perhaps. I’m not entirely sure this is the case, but it is a good place to start. Read the rest of this entry »

Yesterday, I used my new xHR/FB rate to identify and discuss 15 hitters whose xHR/FB rates actually validate their HR/FB rate spikes in 2017. Today, I’m going to check in on the opposite end of the validations — those hitters who suffered severe declines in HR/FB rate that was confirmed by xHR/FB rate. Without xHR/FB rate, we cannot be sure if it’s luck or just skill changes driving the swings in HR/FB, so the metric assists in making that determination.

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As for now, I can’t find any find any predictability to year-to-year hitter consistency once adjusting for plate appearances. For the readers looking for a short article, stop now and move on to Paul’s thesis on starting pitchers. For the stubborn ones, here is what I’ve additionally found out after previously investigating the subject.

On Monday, I could not find any predictability for hitter being consistency. That is not entirely true, I did find that the more plate appearances a hitter accumulates, the more likely they are to reach their true talent level. And if given the opportunity to be closer to their talent level, the more consistent their output.

The one factor I thought might point to year-to-year consistent play in a player’s statistical profile. Are power hitters inconsistent because a few gusts of wind could make a difference in a half-dozen home runs? Do high-walk hitters see their stats as being more consistent since walks stabilize faster? Basically, are certain hitter types more consistent on a yearly basis.

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As suggested by commenter Konoldo in yesterday’s post discussing 10 2018 HR/FB rate decliners, today I am going to use my new xHR/FB rate to identify surprise 2017 power sources validated by the metric. These are the hitters that either came out of nowhere to post big HR/FB rates, or really upped their games, blowing past even the most optimistic of projections. The knee-jerk reaction is always to expect severe regression, but xHR/FB rates might make you think twice. Perhaps a repeat is more likely than you think for these hitters.

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Welcome to the Ottoneu keeper deadline, 2018 edition.  Today (11:59 PM EDT) is the final day to make that difficult decision about your on-the-bubble players before rosters lock and you set your sights on your upcoming league auction.  Per the rules:

Between the end of the Major League Baseball regular season and the end of arbitration, players may be cut. Between the end of arbitration and the keeper deadline, players may be cut or traded. After the keeper deadline and before the auction draft, teams may not cut or trade any players.

Since the keeper deadline also serves as a de-factor trade deadline, I’ve lined up a few final resources for you below and I’ve asked a handful of Ottoneu experts (Justin, Chad, Brad) to check your questions and comments periodically throughout the day to offer their input on your toughest decisions.  You don’t play this game? You should, but even if your non-Ottoneu keeper deadline is still a few weeks away, feel free to fire your questions below and we’ll do our best to give you feedback (for context, don’t forget to let us know details about your league format).

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Yesterday, I used my new and improved xHR/FB rate equation to discuss 10 hitters whose xHR/FB rates sat significantly above their actual marks in 2017, suggesting serious 2018 upside. Today, I’ll talk about the other end of the spectrum, hitters whose xHR/FB rates were well below their actual marks, hinting at real downside risk in 2018, assuming the same underlying skills.

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