This document discusses using Python and PyData tools for baseball analytics. It introduces Shinichi Nakagawa, a baseball analyst and Python expert. It explains common PyData tools like Grafana, Redash and Jupyter Notebook, and how they can be used to visualize and analyze baseball metrics and stats. It also discusses using Python and scraping to analyze run creation (RC) and run creation per 27 outs (RC27) stats to evaluate player and team performance.
This document discusses an XP PyCon JP 2016 conference about Python and agile development. Shinichi Nakagawa, a Pythonista and Scrum Master, will be speaking. The conference will cover topics like agile Python development, DevOps, microservices, and how Python is used for web development frameworks like Django and Flask. It will take place in 2016 and had over 680 attendees at the previous PyCon JP conference. Links are provided to slides and videos from past Python and agile talks.
Shinichi Nakagawa gave a presentation on Python, PyData tools like Jupyter and pandas, and analyzing baseball player Shohei Ohtani's stats from the 2016 season. He introduced himself and his background in Python and PyCon JP. He demonstrated how to create a virtualenv for a Jupyter lab environment to analyze Ohtani's stats from web pages, and shared the GitHub repository containing the Jupyter notebook analyzing Ohtani's batting and pitching stats from that season.
This document summarizes a presentation given at PyCon JP 2016 about analyzing baseball data with Python. The presentation introduced the speaker, Shinichi Nakagawa, and discussed using the MLBAM dataset and Python libraries like pandas and matplotlib to analyze pitching data. Specific examples analyzed the pitching of Yu Darvish before and after Tommy John surgery, compared Ichiro Suzuki and Joey Votto's batting, and looked at pitch location data to study the strike zone. The presentation emphasized the usefulness of Python for accessing and analyzing sports data.
Big Data Baseball with Python - Ichiro Suzuki hacks! #kwsk01Shinichi Nakagawa
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Ichiro Suzuki reached his 3,000th MLB hit on August 7, 2016. The author analyzes Ichiro's journey to 3,000 hits using Python and MLB play-by-play and pitch tracking data from 2015-2016. He extracts Ichiro's at-bat and pitch-level data to analyze trends in Ichiro's pitch selection and performance over the two seasons leading up to the milestone. The author plans to present further analysis and visualizations of Ichiro's path to 3,000 hits at an upcoming Python conference.
This document summarizes Shinichi Nakagawa's presentation on analyzing baseball data with Python. It introduces Shinichi as a Python developer and baseball analyst. It then discusses how he uses the Python pitchpx library and Jupyter notebooks to analyze pitching data downloaded from MLB.com using pandas and matplotlib. Finally, it provides Shinichi's contact information for anyone interested in Python or baseball data analysis projects.
- Shinichi Nakagawa is a 36-year-old Pythonista who works at visasQ inc. and uses Python for his work, especially with Agile frameworks like Lean Startup.
- As a Pythonista for 5 years, he has spoken at conferences like PyCon JP in 2014 and 2015 on topics related to Python and Agile practices.
- He advocates getting out of the building to learn Python, providing output and feedback through blogs and social media, and networking with other Python developers.