Archive for Top College Players

The Top College Players by (Maybe) Predictive Stats

Week: 1 / 2 / 3 / 4 / 7.

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 the most current such 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 »


The Top College Players by (Maybe) Predictive Stats

Week: 1 / 2 / 3 / 4.

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 the most current such 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 »


The Top College Players by (Maybe) Predictive Stats

Week: 1 / 2 / 3.

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 the most current such 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 »


The Top College Players by (Maybe) Predictive Stats

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.

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

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 »


The First Weekend of College Ball by (Maybe) Predictive Stats

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 the first such report for the 2017 college campaign, which began last Friday.

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 »


The Top College Players by (Maybe) Predictive Stats

On multiple occasions last year, the author published a 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.

In recent weeks, I’ve revisited for the 2016 college campaign. What follows represents the most current installment of a possibly infinite series.

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 »


The Top College Players by (Maybe) Predictive Stats

On multiple occasions last year, the author published a 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.

Two weeks ago, I published the first such report for the 2016 college campaign; last week, the second one. What follows represents the third installment of a possibly infinite series.

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 »


The Top College Players by (Maybe) Predictive Stats

On multiple occasions last year, the author published a 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.

Last week, I published the first such report for the 2016 college campaign. What follows represents the second one.

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 »


The First Week of College Baseball by (Maybe) Predictive Stats

On multiple occasions last year, the author published a 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 the first such report for the 2016 college campaign, which began last Friday.

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 »


The (Final) Top College Players by (Maybe) Predictive Stats

On multiple occasions since the middle of March, the author has published here a statistical report designed to serve as a nearly responsible shorthand for people who, like the author, have enthusiasm for collegiate baseball, if not actually expert knowledge of it. These posts have served as a means by which one might broadly detect which players have produced the most excellent performances of the college season.

What follows is another edition of that same thing, updated to account for the completion of every conference’s regular season.

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

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

Since the middle of March, the author has published each week a statistical report designed to serve as a nearly responsible shorthand for people who, like the author, have enthusiasm for collegiate baseball, if not actually expert knowledge of it. These posts have served as a means by which one might broadly detect which players have produced the most excellent performances of the college season.

What follows is another edition of that same thing, updated through Thursday.

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

Read the rest of this entry »


(Maybe) Predictive Stats for Three More College Conferences

Over the past month-plus, the author has published each week a statistical report designed to serve as a nearly responsible shorthand for people who, like the author, have enthusiasm for collegiate baseball, if not actually expert knowledge of it. Those posts have served as a means by which one might broadly detect which players have produced the most excellent performances of the college season.

Because organizing the data for those posts is a bit time-consuming and also because (as I say) my familiarity with college baseball is in its nascent stages, I’ve previously confined those Maybe Predictive posts to three of the most notable conferences: the ACC, Pac-12, and SEC. Since I began publishing them, however, more than one reader has asked for coverage of this or that conference — and as I’ve become more familiar with the game, I’ve wanted that same thing, as well.

What follows is a step in that direction. Included below are the top college players by (maybe) predictive stats for three additional and competitive baseball conferences: the Big 10, Big West, and Missouri Valley. Note that it’s not my intention to suggest that these are certainly the next three best conferences by talent. Note also that it would probably make sense to include the Big 12 here, but the data is even more difficult to access from that conference’s home page.

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

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

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

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

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

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

As in other editions of this same thing, what I’ve done is utilize principles recently introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

As in other editions of this same thing, what I’ve done is utilize principles recently introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »


The Top College Players by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

As in the first three editions of this same thing, what I’ve done is utilize principles recently introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »


The Top Players of NCAA Baseball by (Maybe) Predictive Stats

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances of the college season.

As in the first two editions of this same thing, what I’ve done is utilize principles recently introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »


The Top Performances of College Baseball

What follows does not constitute the most rigorous of statistical analyses. Rather, it’s designed to serve as a nearly responsible shorthand for people who, like the author, have considerably more enthusiasm for than actual knowledge of the collegiate game — a shorthand means, that is, towards detecting which players have produced the most excellent performances over the first weeks of the college season.

As in the first ever edition of this same thing from last week, what I’ve done is utilize principles recently introduced by Chris Mitchell on forecasting future major-league performance with minor-league stats.

Read the rest of this entry »