Market Insights: Betting angles for college basketball with March Madness near

January 22, 2020 12:35 AM

Believe it or not, we are less than two months away from the most exciting sports betting event of the year-- March Madness. 

With college football over and the only football game left being the Super Bowl, bettors will quickly be turning their attention to basketball full-time. This is music to the ears of wiseguys. They've been betting college hoops since opening day in early November. But until recently, the betting public's attention has been divided and preoccupied with football. Now we're seeing college hoops take center stage, which leads to an influx of casual money in the market that pros can look to exploit.

Be sure to bookmark our free VSIN College Basketball Live Odds page so you can monitor lines all season long. 

Wednesday's action features 12 NBA games, 2 NHL games and 53 College Basketball games. For an updated breakdown of sharp action across the marketplace be sure to tune in to the VSiN Market Insights Podcast with Josh Appelbaum. It will be posted at 3:30 p.m. ET.

With college hoops regaining the spotlight, let's discuss a few profitable betting angles. 

Bet against the public in big games: The key to going contrarian, regardless of the sport, is locating the most popular and most heavily bet games of the day where the public is heaviest and then going the other way. The easiest way to do this is to look for nationally televised games, primetime-games and games that feature big-name schools like Duke, Kentucky, Kansas, etc. Also, focus on rivalry games in big conferences (Big Ten, Big 12, SEC, ACC, etc). If the public is loading up one side, the unpopular side has great contrarian value. 

Follow line movement in added or extra games: "regular" college basketball games feature three-digit ID numbers, also known as rotation numbers, NSS numbers or Vegas ID numbers (think "game 781, 782"). However, each day there will be extra or added games with a six-digit ID number (think "game 307183, 307184). These are typically small-school games like Merrimack vs UMass Lowell or UMBC vs Harvard. These games are completely overlooked by the public but not the sharps. As a result, following line movement in these low-bet games can be very profitable because it's solely the result of smart money. If the Lamar vs Incarnate Wood total falls from 145 to 143, it's almost certainly caused by pros with an edge betting the under. The public won't be betting that game. They don't even know it exists. 

Unranked vs ranked: The public is biased toward ranked teams. If they see a number 1 to 25 next to one team but not the other, they will automatically bet the ranked team, no questions asked. The oddsmakers know this innate bias and will shade numbers accordingly. This creates added value to bet on the unranked team because you are getting artificially inflated and better numbers. 

Conference Road Dogs: Conference games are great opportunities to bet on road underdogs. The built in familiarity and rivalry aspect leads to tighter than usual games and more upsets. As a result, backing the team getting points, especially on the road, has great value. If you see the line move toward the conference road dog that's even better because it's a sign of smart money in their favor. According to Bet Labs Sports, conference road dogs against top-5 opponents are 629-546 (53.5%) since 2005. 

Bad ATS vs Good ATS: Casual bettors love betting on teams who cover and love fading teams who don't. In theory, it make sense. However, it's actually the opposite. Teams who cover are overvalued because the oddsmakers know the public will bet on them so they jack up the number. As a result, it's a smart strategy to bet on "bad" ATS teams against "good" ATS teams. If one team is 10-5 ATS and the other team is 5-10 ATS, the latter has more value because they are being overlooked and underpriced. 

Under Systems

Like most other sports, unders are a smarter bet than overs when it comes to college basketball totals. Why? Because the betting public is psychologically biased toward overs because they want to sweat an exciting, fast-paced, high-scoring game. The oddsmakers know this public bias and will shade lines to the over, creating value to buy-low on inflated unders. 

Since 2005, college hoops unders are 24,213-23,868 (50.4%). Overs are 49.6%. Because you have to pay the juice, both are losing endeavors (but you lose less with unders). Remember: the break-even point assuming standard -110 juice is 52.38%. 

The key is focusing on specific situations when it comes to betting the under. Here are a few to look out for.

Under that falls with a big spread: When the home team is a 15-point favorite or more and the total falls at least 1-point the under is 845-714 (54.2%) since 2005, including 59-46 (56%) this season. The idea is simple: the home team gets up big and takes their foot off the gas in the second half, leading to lower scoring games.  

High total falls: When the total is 150 or higher and falls at least 1-point the under is 1,155-1,022 (53.1%) since 2005, including 52-41 (56%) this season

Under with two good shooting teams: when both teams shoot 45% or higher from the floor, the under is 2,452-2,073 (54%) since 2005, including 84-58 (59%) this season.

Unders 150 or more: 4,274-3,886 (52.4%) since 2005, including 150-108 (58%) this season

Unders 160 or more: 803-692 (53.7%) since 2005, including 22-11 (67%) this season

National Champion at Circa Sports

Kansas + 700

Duke + 800

Baylor + 1000

Michigan State + 1200

Gonzaga, Seton Hall + 1500

Butler + 1800

Louisville, Ohio State, Auburn, Dayton + 2000

West Virginia + 2200

Maryland + 2500

Oregon, Kentucky, San Diego State + 3000

Florida State + 4000

Arizona + 5000

Villanova + 6000

Texas Tech, Iowa, Michigan, Wisconsin, Purdue, Wichita State, Illinois, Minnesota, Penn State + 7500

College Basketball Limits at Circa Sports

Standard games: $3K sides, $1K totals, $2K moneylines

Mid-major games: $2K sides, $500 totals, $1K moneylines

Extra games: $1K sides, $500 totals, $500 moneyline

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