Robbie Dudzinski

Galatians 6:9 | Praise God

[Robbie Dudzinski @Robbie_Dudz]

Thank God for Today

“The Lord bless you and keep you; the Lord make his face shine on you and be gracious to you; the Lord turn his face toward you and give you peace” -Numbers 6:24-26

[Robbie Dudzinski @Robbie_Dudz]

@mtabor22 @jaegersports That’s a good looking Trackman report. Wonder who had a hand in that one…

[Robbie Dudzinski @Robbie_Dudz]

Can’t tell what I love more - my brother’s dunk or the translation from Japanese

[B.LEAGUE（Bリーグ） @B_LEAGUE]

上に行くと見せかけた頭脳プレー🧠
佐賀#23 ダジンスキーがダンクを叩き込む！
@DaveTheDude50 @SagaBallooners

📡バスケットLIVEで生配信中
https://t.co/H9U2JuXAaB

#Bリーグ #りそなグループ

[Robbie Dudzinski @Robbie_Dudz]

@tangotiger @drivelinekyle No Pitchers threw both Chanegup and Splitter - something I learned building my machine learning pitch classification model, which was far more successful after engineering it with Bayesian Statistics. Check out the model- https://t.co/FOTUjKRtas

[Robbie Dudzinski @Robbie_Dudz]

Playoff baseball delivered: Tarik Skubal struck out 14 in 7.2 IP, while Garrett Crochet retired 17 straight after giving up a solo HR. Two lefties, two Game 1 road wins, both touching upper 90s with five-pitch mixes. Who had the more impressive outing? @RedSox @tigers @MLB

[Robbie Dudzinski @Robbie_Dudz]

Week 4 of Fantasy Football in a Jupyter Notebook:

Yikes... JGRIV1 put up 73 for a season low total, gets knocked out of the survivor pool, and loses Malik Nabers. What a week. At least his kicker supplied a league high 19 points, which is more than 1/4 of his total.

[Robbie Dudzinski @Robbie_Dudz]

Top 5 Pitching Performances from (9/18 - 9/24) made from Statcast Data:

1. Hunter Greene (9/18) - CG 1H Shutout
2. Emmet Sheehan (9/21) - 7IP, 10K, GS 85
3. Max Fried (9/18) - 7IP, 13K, 26 Whiffs
4. Luis Castillo (9/24) - Clinches Playoffs
5. Garrett Crochet (9/24) - 8IP Shutout

[Robbie Dudzinski @Robbie_Dudz]

Week 3 of Fantasy Football in a Jupyter Notebook:

Did any Fantasy Football sites predict Tre Tucker, Hunter Henry, Mark Andrews, or David Montgomery to be the top performers this week? Interesting to see what predictions flopped week to week, Jupyter tells a story all season.

[Robbie Dudzinski @Robbie_Dudz]

Breaking Down This Week’s Top MLB Pitchers (Sept 11–17, 2025)

1. Tanner Bibee – 9/12 – 93
2. Brandon Pfaadt – 9/17 – 91
3. Kevin Gausman – 9/11 – 91
4. Chris Sale – 9/16 – 85
5. Blake Snell – 9/17 – 83

Read the full breakdown here:
https://t.co/ba7n7x4QG0

[Robbie Dudzinski @Robbie_Dudz]

Week 2 of Fantasy Football Analysis in a Jupyter Notebook

Higher scoring week = one team below 100 points from the remaining teams of the survivor pool. The Japanese Aura Farmers were the low scoring team in week 2.

Be on the lookout for week 3!

[Robbie Dudzinski @Robbie_Dudz]

@CleGuardians Incredible work!

[Robbie Dudzinski @Robbie_Dudz]

Been quite the pitching week! Check out another complete game shutout this week from @IssaBibe with the @CleGuardians - Game Score of 93! Calculated and developed in Python.

@MLB @espn @fangraphs

[Robbie Dudzinski @Robbie_Dudz]

Give this some bigger context, how good is a Game Score of 91? Here is every MLB start in 2025, plotted by Game Score. This ranks as the fourth best in the entire season.

[Robbie Dudzinski @Robbie_Dudz]

Reports are LIVE!

@KevinGausman of the @BlueJays threw a complete game shutout yesterday - resulting in a Game Score of 91! The perfect chance to kickstart posts for this project! See how he did it in my latest project:

Article: https://t.co/aXOOyp1Tkv

@MLB @espn @fangraphs

[Robbie Dudzinski @Robbie_Dudz]

I created a Jupyter Notebook to catalog a Fantasy Football Season🏈

I used the espn_api.football library in Python to import my fantasy football league data. All it took was some DataFrame design and football logic to create these functions.

Onto Week 2!

[Robbie Dudzinski @Robbie_Dudz]

Trackman Hitting Data--Why Aren't You Using It?

Check out my Trackman Report to benefit pitchers and hitters from real game data. Combine this with my pitch metric report for best use of raw game data.

Read about it here:
https://t.co/0bCKorqugC

[Robbie Dudzinski @Robbie_Dudz]

What’s the real value of massive MLB contracts?
Are teams overpaying—or maximizing return?

I’m breaking it down in a 4-part series. Here is part 1: Data collection—web scraping contract values, WAR, and player stats using Python.

https://t.co/v9uhVwmbXq

[Robbie Dudzinski @Robbie_Dudz]

Trackman Pitching Data--Are you Actually Using It?

I wrote a Python script that takes the raw spreadsheet data from a Trackman pitching session and converted it to a one-page pdf. Read about it below!

Message me for details!

https://t.co/w9K1BKn3k4
https://t.co/kb7Mcee0RH

[Robbie Dudzinski @Robbie_Dudz]

Swing Hard, Just in Case You Hit it:

Check out my latest post on swing speed in MLB, as I use mathematical modeling to answer questions for training and having success in baseball.

Check out training ideas with @billmills for more!

https://t.co/tUiu7xBCav

[Robbie Dudzinski @Robbie_Dudz]

Radar gun program is active this morning for live ABs with @billmills

[Robbie Dudzinski @Robbie_Dudz]

In the offseason, a bullpen session may be only 20-30 pitches. Lots of guys track these throws and focus on the data. What about the hundreds, if not thousands of throws made during the week, every week of the offseason? This is my solution:

https://t.co/2OlNQfoPoM

[Robbie Dudzinski @Robbie_Dudz]

In the offseason, a bullpen session may be only 20-30 pitches. Lots of guys track these throws and focus on the data. What about the hundreds, if not thousands of throws made during the week, every week of the offseason? This is my solution:

https://t.co/2OlNQfoPoM