In this example the Jets are listed as four-point favorites (-4) over the Bills and the 49ers are three-point underdogs (+3) against the Seahawks. So, if you bet $110 on the favored Jets, they must defeat the Bills by more than four points in order to win $100. If you bet $110 on the underdog 49ers you will win $100 if they win outright or lose by less than the three-point spread. If the final score happens to end up exactly on the number it's a tie, or 'push,' and you get your money back.
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Machine learning models can make predictions in real time based on data from numerous disparate sources, such as player performance, weather, fan sentiment, etc. Some models have shown accuracy slightly higher than domain experts. These models require a large amount of data that is comparable and well organized prior to analysis, which makes them particularly well suited to predicting the outcome of Esports matches, where large amounts of well structured data is available.
The negative (-) sign indicates that the Cowboys are the favorites, while the positive (+) sign indicates that the New York Giants are the underdogs. With the spread set at 2.5 points, a bet on the Cowboys would mean that they would have to win by more than 2.5 points (3 or more) in order for you to win that bet. A bet on New York would mean that the Giants would have to either lose by 2.5 or less points (2 or less) or win the game outright in order for your bet to win.
What's the difference between an oddsmaker and a bookie? A bookie's job is purely mechanical. He gets the line from Vegas or another source, offers bets at those odds, then collects money from the losers, pays out to the winners and keeps the vig. Oddsmakers don't actually take bets -- they study the games and set the lines. Bookies often adjust the spreads for their games, so they do a little oddsmaking, and oddsmakers work for casinos, which operate sports books that take bets.
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