-47.61 Performance
SaleneBettingGroup has a total of 13 completed sports picks the last 30-days.
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Other* Return on Investment (ROI) figures above represent potential returns based on a $100 per unit risk amount. Please note that past results do not guarantee or imply future performance.
Lastest Picks
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11/20/2025 (NCAAB) ROBERT MORRIS VS. ST BONAVENTURE
 -11.00 ROBERT MORRIS +12.5 (-111)
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11/20/2025 (NCAAF) LOUISIANA VS. ARKANSAS STATE
-11.00 ARKANSAS STATE -2.5 (-104)
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11/20/2025 (NFL) BUFFALO BILLS VS. HOUSTON TEXANS
 +9.91 HOUSTON TEXANS +5.5 (-111)
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11/10/2025 (NHL) FLORIDA PANTHERS VS. VEGAS GOLDEN KNIGHTS
-11.00 OVER 5.5 (-119)
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11/9/2025 (NFL) ATLANTA FALCONS VS. INDIANAPOLIS COLTS
 +0.00 ATLANTA FALCONS +6 (-104)
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11/9/2025 (NFL) NEW ENGLAND PATRIOTS VS. TAMPA BAY BUCCANEERS
-11.00 TAMPA BAY BUCCANEERS -2.5 (-116)
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11/8/2025 (NCAAF) GEORGIA VS. MISSISSIPPI STATE
-11.00 MISSISSIPPI STATE +9 (+100)
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11/8/2025 (NCAAF) MARYLAND VS. RUTGERS
-11.00 MARYLAND +2 (-112)
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11/8/2025 (NCAAF) FLORIDA STATE VS. CLEMSON
+9.82 CLEMSON -1.5 (-112)
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11/8/2025 (NCAAF) LSU VS. ALABAMA
-11.00 LSU (+311)
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11/8/2025 (NCAAF) FLORIDA VS. KENTUCKY
+10.38 KENTUCKY +3.5 (-106)
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11/7/2025 (NBA) HOUSTON ROCKETS VS. SAN ANTONIO SPURS
+10.28 SAN ANTONIO SPURS +3 (-107)
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11/6/2025 (NFL) LAS VEGAS RAIDERS VS. DENVER BRONCOS
-11.00 DENVER BRONCOS -10 (+112)
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Bio
Salene Betting Group
Data-Driven Sports Betting
Salene Betting Group is a cutting-edge sports betting pick service powered by advanced computer-generated algorithms and sophisticated statistical modeling. This model took over four years to complete.
Our system processes thousands of variables — including player performance metrics, team dynamics, injury reports, weather conditions, and betting market trends — to deliver highly accurate predictions across major sports leagues.
By continuously learning from real-time data and historical outcomes, our proprietary models are optimized to identify profitable betting opportunities with an average win rate between 70% and 85% over time. Every selection is backed by quantitative analysis and machine learning refinement. There is only 20% human influence in the decision-making process allowing for an approach that removes most of the emotion from the selection.
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