In Case You're New Here...
I'm an actual statistician and real statistics professor. I have built mathematical models (called SideLine) for predicting various sports outcomes. All projections are displayed in a Google Sheet specific to that sport and season (see the homepage for links).
- For college basketball, the player-based model incorporates pace of play and both offensive and defensive metrics in order to predict the average margin of victory as well as the total number of points scored in the game. SideLine will make a graded money line pick on every game, where the A grades are always recommended plays but the others are only occasionally. All picks will be made in order to pay 3 units, that is, the risk and the win amount will add to 3. (This allows us to wager less on bigger underdogs than flat betting.)
- For MLB, the player based model uses historical and current season data that is rolled up to the team level based on current active rosters. It assumes normal starting lineups, so my recommendation for these predictions is to use them before starting lineups are announced (or only after seeing that the lineups are relatively normal). For players injured or coming off the IL, check the Notes cell in the Teams tab of the MLB Google Sheet to see if I have manually adjusted a roster to adjust for a move the team hasn't officially made yet. SideLine also pulls weather data in order to better predict the average number of runs scored in each game. Note that for MLB money line plays, I normally suggest a "selected" bet type unless the starting pitcher for the team you are backing is subpar. This will give you a push if that pitcher doesn't start, and my results will reflect this practice. All run line and total bets operate as "listed", meaning that both pitchers listed in the Sheet must go for a play to be active, as this is the typical way sportsbooks (though not all) operate. All picks will be made in order to pay x units, where x is larger based on the edge that game has. That is, the risk and the win amount will add to that number (depending on the grade the pick receives).
- For FBS college football, the (new for 2023!) player-based model gives each team a rating, where the difference between two ratings (adjusted for the expected number of possessions) is the projected margin of victory in a game between those two teams on a neutral field. (This is adjust for expected pace of play.) These ratings are not a resume ranking, but rather a forward facing metric that is the best guess of how well the team will perform in their next game. A similar betting style to baseball is used for these bets.
For other benefits beyond the predictions on every game as well as the free picks on the YouTube shows, visit DubClub. Benefits include the play of the day, all model projections and picks, and access to our Discord server -- the best place for answers to your questions. I'm unable to always respond quickly to tweets and YouTube comments, but the Discord is like a text message -- if I'm able to be on my phone, I see it, and I'll respond. It's also a great place to get input from other sports bettors, along with Cousin Jared and your friend Jake. You also gets ad-free shows. For college basketball and MLB, you'll get projections usually before noon the day before the games, allowing you to jump on early mispriced sides and totals. For college football this means model projections on Sunday afternoon in order for you to grab key numbers before they disappear early in the week. The prices tend to move towards my model (though not always!) so this can be a good resource to know when to jump on a number and when to wait it out. Getting the best of the number doesn't guarantee betting success, but it can be a big part of the puzzle.
Because it projects every single game on three sports (two of which have over a thousand games every season) the model is going to be wrong a lot. Me and my cohosts will be wrong a lot. I'm acknowledging that right here up front. My goal for the YouTube community is to foster a fun and educational environment. A non-exhaustive list of examples of good comments include adding new pieces of information to the discussion, your own predictions, and questions about the games or the picks. (Though the Discord chat is the best place to get questions answered quickly.) Rude comments, lies, misinformation, and pointing out games that I, my cohosts, or the model were wrong on simply are not welcome; likewise I won't comment when you're wrong. We're gonna be wrong a lot, and if you're looking for a place that's going to go 65% on -110 bets, it's not this one. (Or any place, but if that's what you're looking for then I wish you the best of luck in that search.)
Lastly, I close every show with the same saying -- you can eat your betting money, but please don't bet your eating money. It's a little silly but illustrates a helpful point: it is possible to eat dollar bills, but wagering money that goes towards basic life needs is not recommended. Please don't try to turn a certain amount of money into enough to pay off a credit card bill (or whatever other thing needs to get paid). Every dollar spent on sports gambling should be separate from other finances, as there's a reason why people say you shouldn't bet money you can't afford to lose. There are no such things as locks in gambling, and every professional in the world still has losing months. Most have had losing years! And almost every sports gambler who has been doing this for any amount of time will say that they had to learn a lot before they reached break even or profitability. It often last years and incurs a lot of losses. Track your wagers -- I'm a fan of spreadsheets with tabs and formulas and formulaic color coding, but free services like betstamp also do the trick -- and start small, being aware that the early years of sports gambling usually are the worst. If you do find yourself betting your eating money, or are concerned you might have a problem, please visit this website. Know that seeking help is not a sign of weakness.
Professor Sides played small time college baseball while earning a Mathematics degree before earning his Ph.D. in statistics from Baylor University where he met his wife (Mrs. Professor). He spent five years working as a statistician in the "real world" before spending another five years in higher education. The drive to help others, background in math and statistics, and love for sports led him to build the mathematical models that predict various sports outcomes. He can be found on Twitter @ProfessorSides.
Cousin Jared (the professor's actual cousin) took one statistics course in college, but not by choice. His perfect Saturday includes long walks on the beach and Mountain West football. Michael Bishop was an amazing quarterback. Model whisperer. Ask him about Christmas movies. (Editor's note: maybe don't, actually?) He can be found on Twitter @CousinJared.
Jake (the professor's actual friend) is a college basketball addict who decided should be watching the game instead of playing it after his third ACL tear. When he is not watching college basketball he enjoys taking his puppies on walks and smoking meat. His one true love is Teenage Mutant Ninja Turtles, and he either has or doesn't have the tattoo the prove it. He can be found on Twitter @MyFriend_Jake.