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Using Pre-NBA Draft Data to Project Success in the NBA
| Content Provider | Semantic Scholar |
|---|---|
| Author | Edwards, Ryan M. |
| Copyright Year | 2015 |
| Abstract | The NBA draft poses a unique challenge in that predicting a player’s future success in the NBA is incredibly difficult. We seek to use machine learning techniques to quantify the attributes that tend to indicate a college player’s playing ability in the NBA. Using historical data from players’ college careers in combination with their NBA career data, we have developed a model to predict where a player should be drafted (if at all) in the NBA draft. While predicting the exact number of win shares per season that a player will contribute was difficult, predicting whether a player would be successful, or what discretized level of success that player would attain in the NBA was easier to predict withWheeler, Kevin. “Predicting NBA Player Performance,” 2012. [Online]. Available: http://cs229.stanford.edu/proj2012/WheelerPredictingNBAPlayerPerformance.pdf higher certainty. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://cs229.stanford.edu/proj2015/120_report.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
