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<meta property="og:description" content="Back on track: 52/96, or 54% accuracy overall this past week. We’ve now made a number of improvements to the algorithm, and we’re excited to see how our performance continues from here on out. New predictions are posted. Point spread # games Accuracy <= 1 34 53% <=2 16 …" />
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<meta name="twitter:description" content="Back on track: 52/96, or 54% accuracy overall this past week. We’ve now made a number of improvements to the algorithm, and we’re excited to see how our performance continues from here on out. New predictions are posted. Point spread # games Accuracy <= 1 34 53% <=2 16 …">
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<span class="caps">MLB</span> week 5 review, week 6 posted
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<p>Back on track: 52/96, or 54% accuracy overall this past week. We’ve now made a number of improvements to the algorithm, and we’re excited to see how our performance continues from here on out. New predictions are <a href="http://efavdb.github.io/weekly-nba-predictions">posted</a>.</p>
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<th>Point spread</th>
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<td><= 1</td>
<td>34</td>
<td>53%</td>
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<td><=2</td>
<td>16</td>
<td>56%</td>
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<td><=5</td>
<td>26</td>
<td>50%</td>
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<td>>5</td>
<td>20</td>
<td>60%</td>
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Jonathan grew up in the midwest and then went to school at Caltech and UCLA. Following this, he did two postdocs, one at UCSB and one at UC Berkeley. His academic research focused primarily on applications of statistical mechanics, but his professional passion has always been in the mastering, development, and practical application of slick math methods/tools. He currently works as a data-scientist at Stitch Fix.
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<h4>Published</h4>
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