Week: 1 / 2.

Over the last couple years, the author has published a periodic statistical report designed to serve as a mostly responsible shorthand for people who, like the author, possess more enthusiasm for collegiate baseball than expert knowledge of it. Those reports integrated concepts central to much of the analysis found at FanGraphs — regarding sample size and regression, for example — to provide something not unlike a “true talent” leaderboard for hitters and pitchers in select conferences.

What follows represents such a report for the 2017 college campaign, following roughly three weeks of play.

As in the original edition of this same thing, what I’ve done here is to utilize principles introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

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Episode 721
Managing editor Dave Cameron is the guest on this edition of the pod, during which he discusses the question of assigning credit, especially where WAR is concerned. How much credit does Andre Ethier for a wind-aided home run? For catching a fly ball that’s caught 95% of the time by major-league outfielders? These are merely two of the questions asked poorly and slowly by the host and half-answered by Dave Cameron.

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Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 42 min play time.)

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Don’t hesitate to ignore all this introductory matter.

Over the last couple years, the author has published a periodic statistical report designed to serve as a mostly responsible shorthand for people who, like the author, possess more enthusiasm for collegiate baseball than expert knowledge of it. Those reports integrated concepts central to much of the analysis found at FanGraphs — regarding sample size and regression, for example — to provide something not unlike a “true talent” leaderboard for hitters and pitchers in select conferences.

What follows represents an updated report for the 2017 college campaign.

As in the original edition of this same thing, what I’ve done here is to utilize principles introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »

Episode 720
Managing editor Dave Cameron is the guest on this edition of the pod, during which he discusses the relevance of PITCHf/x and Statcast data to understanding the probability of Mets right-hander Robert Gsellman approximating last year’s success in 2017. Also discussed: how only one out of every five professional pitchers doesn’t experience the horrors of attrition before reaching the majors.

A reminder: for the cost of a very expensive cup of coffee, readers can experience FanGraphs without ads. Click here to learn more about an Ad Free Membership.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 36 min play time.)

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Podcast: Play in new window | Download

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Episode 719
Lead prospect analyst Eric Longenhagen is the guest on this edition of the pod. On this episode, he and the idiot host set the over/under WAR figures for the home edition of the Over/Under Prospect Game, which readers can play by clicking here.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 1 hr 10 min play time.)

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During the former’s most recent appearance on FanGraphs Audio, Eric Longenhagen and the host of that program played what they called the Over/Under Prospect Game. The rules of the game are discussed in greater detail within the pod episode. In short, though, this is how it’s played:

• Contestant A nominates a rookie-eligible player.
• Contestant A also sets an over/under figure for that player’s WAR in 2017.
• Contestant B chooses the over or under.

The results of that experiment are published here. But why this current post exists is because multiple readers suggested that they, too — with a view to holding at arm’s length the burdens and worries of life — would like to participate in the Over/Under Game, as well. So what this post does is announce the existence of such a game for readers of FanGraphs.com.

Play the Over/Under Prospect Game: Home Edition by clicking here.

For this edition of the Game, Longenhagen and I have set over/under WAR figures for 10 players, five batters and five pitchers. What sort of batters and pitchers? Rookie-eligible ones, is what kind — specifically, the rookie-eligible ones who’ve received the highest plate-appearance and innings-total projections per this site’s depth charts.

The rules of the Home Edition are as follows:

1. Choose the under or over for each prospect listed.
2. Choose an innings total for Ty Blach — as tie-breaker in event that multiple contestants finish with most correct guesses.
3. The winner will receive a gift certificate for Barrio Queen in Scottsdale, Arizona, equivalent to price of pitcher of sangria plus tax.

Ballots for the Home Edition of the game will be accepted until Sunday, March 5, at 11:59pm ET. Any questions? Don’t hesitate to ask them in the comments section below. Also don’t hesitate to listen in breathless, real-time action to the making of the Home Edition on FanGraphs Audio.

Play the Over/Under Prospect Game: Home Edition by clicking here.

As of this moment, the writers at FanGraphs responsible for curating this site’s depth charts have allocated some type of playing time to 586 position players across the league’s 30 teams, from Freddie Freeman, Miguel Sano, and Carlos Santana at 665 plate appearances each down to Victor Caratini and Pedro Severino at just six a piece.

Of the 586 players who appear on those depth charts, 264 have received a sufficient quantity of fan ballots to earn a published projection. The fan assessments tend almost uniformly to skew optimistic. In the case of the 264 aforementioned players, for instance, the depth-chart projections (a combination of Steamer and ZiPS) call for an average of 2.1 WAR per every 600 plate appearances. The fans, meanwhile, call for 2.7 WAR per 600 plate appearances. That’s roughly a half-win difference for every player prorated to a full season.

Even though optimism is generally the rule in these matters, it comes in degrees. Nearly 20 players, for instance, receive a prorated fan projection that’s precisely a half-win better than the corresponding depth-chart projection. Over 170 players — i.e. about 65% of the entire sample — earn a fan-based projection that’s between 0.0 to 1.0 wins better than the figure produced by the the combination of Steamer and ZiPS.

The purpose of this brief post, however, is to consider briefly the players about whom the crowd is most optimistic. To identify that group, I prorated both the fan and depth-chart projections to 600 plate appearances and subtracted the latter result from the former. Here are the players who receive the top fan projections relative to their depth-chart numbers.

Perhaps unsurprisingly, one finds that most of the players here — all of them, really, with the exception of Luis Valbuena — have little major-league experience. That makes sense on two accounts. First, it’s logical that the projection systems would be conservative with this population. All things being equal, a player who lacks past success in the majors is unlikely to produce future success in the majors. On the other hand, if there’s a type of player about whom the public might possess information for which the numbers don’t account, it’s a prospect.

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