How this is useful:

• Can be used as a scouting report to look at pitch type sequence tendencies

How I created it:

• Created sequences for each at bat in season for given pitcher

• Turned sequences into data frame that had all combinations of a pitch with the next pitch. Ex: (0-0 FF -> 1-0 SI), (1-0 SI -> 2-0 FF)

• Used that data frame to create a sankey network

How this is useful:

• Validating a new metric

• Can see the average positioning of defenders coincide with 'cool' zones

How I created it:

• Cleaned dataset of ~120,000 batted balls, removing all instances that were deemed to be 'No Nulls'

• Ran an XGBoost multi-class classification using a multi:softprob objective that returned the probability of each batted ball belonging to one of five classes: out, single, double, triple, or homerun,

• Applied weighted on-base average (wOBA) linear weights averaged over past six seasons to each class probability to get a weighted likelihood value

• Took the weighted likelihood value and scaled it to an average of 100, similar to other popular metrics (wRC+, OPS+, DRC+, etc)

How this is useful:

• Can be used as a scouting report to look at pitch type sequences

• Ex: Can see that Hendricks mainly throws curveballs to lefties as a first pitch

How I created it:

• Scraped all of Hendricks' pitches from the 2019 season via baseballsavant.com

• Created a ggplot with geom_tile() to plot each pitch as a rectangle and then applied a facet_grid() to separate by batter handedness

How this is useful:

• Can be used as a sort of game summary to see how each hitter was pitched against

• Ex: Can see that Hendricks attacked Myers with a majority of sinkers

How I created it:

• Scraped all of Hendricks' pitches from the 2019 season via baseballsavant.com

• Created a ggplot with geom_tile() to plot each pitch as a rectangle and then applied a facet_grid() to separate by batter

How this is useful:

• Shows Contreras' framing throughout the 2019 season (did he improve as Theo Epstein said?)

• Can be used to see if changes being implemented by either player or coach is actually working

How I created it:

• Limited data to only called balls and strikes in the 'Shadow Zone'

• Calculated rolling average by number of strikes in last 50 called pitches in the 'Shadow Zone'

How this is useful:

• Shows a game-wide trend of velocity diminishing as pitches increase

• Shows how few pitchers in today's game reach a high number of pitches

How I created it:

• Downloaded every pitch in the Statcast era (2015-2019)

• Found the average velocity of each pitch in the game (1st, 2nd, etc.)

• Found what percentage of pitchers throw (1, 2, etc.) pitches in a game

How this is useful:

• Shows a 'warming up' effect for starting pitchers

• Shows how velocity seems to recover after inning breaks

• Can lead to see how individual pitches velocity is affected as the number of pitches thrown increases

How I created it:

• Downloaded every pitch in the Statcast era (2015-2019)

• Found the average velocity of each pitch in the game (1st, 2nd, etc.) separated by pitch type

How this is useful:

• Can be used as a scouting report to look at pitch type tendencies by count

How I created it:

• Scraped all of Hendricks' pitches from the 2019 season via baseballsavant.com

• Used facet_grid on a ggplot object separated by ball and strike combinations

How this is useful:

• Shows a catcher's strengths and weaknesses in framing - most important aspect of catcher defense

• Helps front office and coaches evaluate a catcher's framing ability

How I created it:

• Limited data to only called balls and strikes in the 'Shadow Zone'

• Separated pitches into eight distinct zones

• Calculated strike percentage in each zone for player and league

How this is useful:

• Shows at a macro level this catcher's called pitches break down

How I created it:

• Limited data to only called strikes and balls in the 'Shadow Zone'

• Labeled each remaining pitch as either: a ball in the strike zone, ball out of the strike zone, called strike in the zone, or called strike out of the zone

How this is useful:

• Shows at a granular level where the catcher is successful and not at framing borderline pitches

• Can lead coaches/front office to offer advice on where this catcher needs to improve his framing

How I created it:

• Limited data to only called balls and strikes in the 'Shadow Zone'

• Then limited the data further to only called strikes outside of the strike zone and balls inside the strike zone

How this is useful:

• Quick velocity summary of the game for the pitcher and coaching staff

How I created it:

• Labeled each pitch of the game

• Plotted velocity for each pitch divided by pitch type

• Made a plotly of the ggplot to add the interactive feature of the graph

Part of a game summary R Shiny app (in development)

How this is useful:

• Can help the opposing team to try and identify differences in release point in order to pick up the pitch type from release

• Helps the pitching coach/front office identify flaws in release point and try make them as similar as possible

How I created it:

• Made a 3D plot of the pitcher's release point height, side, and extension

• Color coded by pitch type

Part of a game summary R Shiny app (in development)