Big favourites, at least on the Money Line, are still struggling to recover from early losses, although the Run Line bets are slightly positive.
One reason may be the general decline in Hits this season, a key metric in baseball, and no surprise that with the Designated Hitter rule being applied in both leagues, the National League is leading the American league in this category as well as in Runs per Game for the first time since the two leagues were realigned to have the same number of teams.
The trouble with this shortened season is that it'll be over by the time we have any meaningful data. Here are some stats for the season to date:
Hits are below 8.0 per team per game, doubles and triples also at a record low, and average Strike-Outs close to a record high.
Empty stadium effect: Fielders are making more plays in part because they can hear the crack of the bat. The fan-less backdrop may also help them see the ball more clearly.
I'm not sure if the decline in Hits, Doubles and Triples (Home Runs are actually at a high this year) is related to the performance of hot favourites, but as I read just yesterday in The Creativity Code (Art and Innovation in the Age of AI) by Marcus du Sautoy, there isn't always an understanding for why something is true, even if it is proven:
For now though, AI offers mathematicians an incredible tool through which to magnify their own creativity and skills. Some mathematicians believe the field has already reached levels beyond any human’s ability to stretch. Many proofs, for example, require so many combinations and calculations they can take hundreds of pages of complex equations and years to resolve, even with algorithms. Even so, some mathematicians resist using computers and algorithms, because though they might prove something, machines can’t say why. In other words, they can prove the results, but they can’t confirm the understanding. For most mathematicians, understanding matters as much or more than the fact of proving or disproving a statement. Nevertheless, a growing body of researchers has come around to the reality that the future of math includes man and machine. Just as people climb Mt. Everest on their own, but need machines to reach the moon, math now needs algorithms to scale new heights.
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