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Matthew Sherman
Apr 24, 2024

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Football

2024 Mock Draft Competition
Predicting the first round of the NFL Draft with friends.
Just a friendly competition.
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Matthew Sherman
Nov 10, 2023

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Football

Download: NFL Transactions and Immaculate Grid Guide
The end all be all app for tracking NFL player movement.
Mission Statement: Made for Python 3.9.12, this app tracks NFL Transactions from 2000 - present. Its purpose is to discover which teams often share players and uncover trends in player movement. It also seconds as a cheat guide for the Immaculate Grid game.
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Matthew Sherman
Nov 10, 2023 ‧ 1 min read

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BTC

Behind The Code: NFL Transactions and Immaculate Grid Guide
A look at the code and thought process behind this project.
Friendly reminder: All written code is intended for personal use. Efficiency and neatness are not the focus of these projects.
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Matthew Sherman
Nov 10, 2023 ‧ Images Only

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Charts

Charts! NFL Transactions and Immaculate Grid Guide
Just the pictures!
Graphical examples of the application in use.
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Matthew Sherman
Sept 1, 2023 ‧ 6 min read

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Football

Data Analysis

DA

GIS

Sprawlball NFL - Utilizing GIS for the NFL's Benefit
Inspired by the works of Kirk Goldsberry, Sarah Mallepalle, Ron Yurko, Konstantinos Pelechrinis, and Samuel L. Ventura
Abstract: Baseball has long used geographical data on a small scale to track information like pitching zones. Recently, the sport's use of geography has grown both in prevalence and scale with the growing popularity of spray charts to plot against the fear-mongering defensive shift. Following baseball was basketball, which now uses geographical data to track shooting zones. The NFL has recently hopped on the geography train, however it gatekeeps its source data behind the veil of Next Gen Statistics. Fortunately, this data can be gathered through the practice of image analysis, allowing it to be analyzed in new ways by independent sources. Which teams use the same play calls on repeat? Whose patterns are redundant and predictable? Did Matt Canada actually keep Kenny Pickett from throwing the ball over the middle of the field? Geographical data will answer all of these questions and more.
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Matthew Sherman
Sept 1, 2023 ‧ 1 min read

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Data Analysis

DA

GIS

BTC

Behind The Code: Sprawlball NFL
A look at the code and thought process behind this project.
Friendly reminder: All written code is intended for personal use. Efficiency and neatness are not the focus of these projects.
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Matthew Sherman
Sept 1, 2023 ‧ Images Only

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Charts

Charts! Sprawlball NFL - Utilizing GIS for the NFL's Benefit
Just the charts!
The graphics for Sprawlball NFL - Utilizing GIS for the NFL's Benefit. Inspired by the works of Kirk Goldsberry, Sarah Mallepalle, Ron Yurko, Konstantinos Pelechrinis, and Samuel L. Ventura
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Matthew Sherman
Mar 29, 2023 ‧ 7 min read

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Football

Data Analysis

DA

A New Stat: Quarterback Consistency
Is your team's quarterback consistently good or consistently bad? Maybe they're just consistent.
Abstract: Consistent play is a key to consistent victory. In spite of this, no popular consistency statistic exists in football today. Using a newly created formula, one can easily measure the consistency of a quarterback. While the heart of the formula will remain the same, users can also alter multiple smaller variables to help the rating best fit their needs. Ultimately, this new statistic focusing on measuring the consistency of NFL quarterbacks would be of great aid to any team looking to build a better, stronger, and more consistent roster.
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Matthew Sherman
Mar 29, 2023 ‧ 1 min read

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Data Analysis

DA

BTC

Behind The Code: A New Stat: Quarterback Consistency
A look at the code and thought process behind this project.
Friendly reminder: All written code is intended for personal use. Efficiency and neatness are not the focus of these projects.
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Matthew Sherman
Mar 29, 2023 ‧ Images Only

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Charts

Charts! A New Stat: Quarterback Consistency
It's actually tables this time!
The graphics for A New Stat: Quarterback Consistency
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Matthew Sherman
Jan 27, 2023 ‧ 11 min read

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Football

Data Analysis

DA

Scouting Quarterbacks by Scouting Their Wide Receivers
Explore the influence that wide receivers have on college quarterbacks.
Abstract: While reception shares are often overlooked, this project uncovers a statistic that is incredibly useful in narrowing down the field of draftable quarterbacks, allowing NFL teams to determine which quarterback will fit best with their already established receiving corps, which quarterbacks were carried by their WRs in college, and which quarterbacks spread the ball too much, missing opportunities to get the ball into the hands of their greatest playmakers.
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Matthew Sherman
Jan 27, 2023 ‧ 2 min read

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Data Analysis

DA

BTC

Behind The Code: Scouting Quarterbacks by Scouting Their Wide Receivers
A look at the code and thought process behind this project.
Friendly reminder: All written code is intended for personal use. Efficiency and neatness are not the focus of these projects.
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Matthew Sherman
Jan 27, 2023 ‧ Images Only

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Charts

Charts! Scouting Quarterbacks by Scouting Their Wide Receivers
Just the charts!
The graphics for Scouting Quarterbacks by Scouting Their Wide Receivers
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Matthew Sherman
Sept 28, 2022 ‧ 4 min read

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Football

Data Analysis

DA

The Data Science of Scouting Quarterbacks: Part 1
Part 1: Scouting Quarterbacks by Scouting Wide Receivers
Abstract: Quarterback is the most important position in sports. Despite this, it remains a notoriously difficult position to scout. Instead of scouting quarterbacks directly, explore using the easier-to-scout wide receiver position to evaluate our future signal callers.
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Matthew Sherman
Sept 28, 2022 ‧ 4 min read

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Football

Data Analysis

DA

The Data Science of Scouting Quarterbacks: Part 2
Part 2: Where Should You Draft Your Quarterback?
Abstract: At what draft pick have teams had the most success drafting quarterbacks? When should a team trade up for a quarterback? Is it really worth it to move up to pick 32 and secure that fifth year option on a quarterback with a mid-round grade? This section explores how quarterback value aligns with draft pick value.
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Matthew Sherman
Sept 28, 2022 ‧ 6 min read

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Football

Data Analysis

DA

The Data Science of Scouting Quarterbacks: Part 3
Part 3: When Should You Move On From a Quarterback?
Abstract: Teams often hold on to starting quarterbacks for far too long. This leads to far too many decade-long Super Bowl appearance droughts. Ultimately, many teams and quarterbacks alike would be better off with earlier divorces. That being said, when exactly should you move on from your quarterback?
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Matthew Sherman
Sept 28, 2022 ‧ 4 min read

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Football

Data Analysis

DA

The Data Science of Scouting Quarterbacks: Part 4
Part 4: Drafting Around Your Quarterback
Abstract: It takes an incredibly rare quarterback to be able to win with limited surrounding talent. By taking a look at the draft strategies followed by Super Bowl winning teams, we can learn how to put young quarterbacks into friendlier situations. Ultimately, altering one's draft strategy may just turn a talented quarterback into a Super Bowl quarterback.
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Matthew Sherman
Sept 28, 2022 ‧ 2 min read

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Football

Data Analysis

DA

The Data Science of Scouting Quarterbacks: Part 5
Part 5: Quick Graphs
Abstract: Not all stats warrant a full section of discussion. This section explores quick and easy data analysis on topics such as player value vs pick drafted, QB value vs height, QB value vs 40-time, and so much more.
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Matthew Sherman
Sept 28, 2022 ‧ Images Only

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Charts

Charts! The Data Science of Scouting Quarterbacks
Just the charts!
The graphics for The Data Science of Scouting Quarterbacks (all parts)