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NCAA Division I Tournament Bracket Modeler
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README.md

README.md

baseball_bracket v0.1

NCAA Division I Baseball Tournament Bracket Modeler

by Carl James

v0.1 release only partially tested

This app is written in Python 3.7.1, but should run on Python 3.6.0 and later. Use of f-strings will prevent it from functioning unaltered on prior Python versions.

This app web scrapes data from some college baseball ranking sites and processes the data in such a way as to attempt to emulate the process the NCAA committee goes through to build a field of 64 for the NCAA Division I Baseball Tournament.

The output is a text file with a field of 64.

This takes current data of games played to the date of running the app. It does not make any predictions about games not yet played. Essentially this is what the field would look like if the season ended today.

In the off-season the field generated is as if the field was re-established with the same criteria after the Men's College World Series(CWS). For example even though Oregon State won the 2018 CWS title, this model only places the Beavers as the national #5 seed - largely because they were not the Pac-12 champions. The #1 and #2 teams both got a boost due to winning their respective conference titles even though #2 Stanford didn't even win their own regional in the tournament.

Factors used in selecting/ranking/seeding the teams:

  • automatic bids
  • NCAA Rating Percentage Index(RPI)
  • Boyd's World Iterative Strength Rating(ISR) for western teams
  • Regular Season Conference Championships
  • winning or losing record in own conference
  • record against the RPI top-50 and top-100 both win percentage and percentage of total games against these quality teams

Factors in regional assignment

  • hosts are all the top-16 national seed
  • priorty given to teams in same state as host
  • second priority given to teams in adjacent states
  • remaining teams are assigned regardless of geography in a reverse order to the hosts so that if the lowest ranked team is not assigned by geography, they will be assigned to the highest national seed.

Tests using pytest are written only for data collection at this point, but now that I have .json files to work with I plan to add tests for the rest of the application as well. A concern is while it works well for the current data, there are edge cases I can envision which may cause problems that do not exist in this data set.

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