Analytics data
for the edge-seekers.

Quickly assess how a team performs at home vs on the road before evaluating a spread. A team with a strong home record but a losing away record is a very different bet depending on location.

When a high-pace team plays a slow defensive team, the total line often doesn't fully account for pace suppression. Pull both teams' pace profiles, calculate the mismatch, and compare against the posted total.

An away team playing their second game in two nights while the home team has had three days off is a significant structural disadvantage. Systematically identify those spots across the full slate rather than manually checking schedules.

Before betting a close line on a divisional rivalry game, pull the head-to-head record to see if there's a persistent historical edge. A team that's 18-6 against a specific opponent over 4 seasons is a meaningful data point.

If Scott Foster has a 68% home win rate in his career sample, that's a structural edge for the home team regardless of who's playing. Pull tonight's referee assignments and factor their home win tendencies into your model before the line moves.

A referee with a 61% over rate across 200+ games reflects their foul-calling style inflating scoring through free throws. If tonight's ref has a persistent over lean and the total is on a key number, that's actionable.

Query DET with Scott Foster and see Detroit's historical record when he's officiating. If a team is 3-14 with a specific referee over three seasons, that's a statistically significant pattern worth factoring in.

If LAL covers at 65% when a specific official is assigned to their home games, that's not something the closing line accounts for. Build a lookup table of team/referee ATS combinations and query it nightly as referee assignments post.

An away team that flew 2,400 miles west from Miami to Denver the previous night, playing at altitude against a rested home team, is at a structural disadvantage on multiple dimensions. Stack this with rest data and timezone lag.

A West Coast team playing a 1pm ET road game on the East Coast is essentially playing at 10am their body time. Use the lag advantage score to filter for the highest-impact timezone spots without manually tracking every team's schedule.

Build a backtest: pull all games where a West team traveled east on a back-to-back, cross-reference with the ATS result from this endpoint, and measure historical cover rate. That three-endpoint workflow is how systematic bettors validate edges.

A team flying from Miami (sea level) to Denver (5,280 ft) faces a physiological disadvantage that doesn't appear in any box score. Identify the highest altitude-change spots and factor that into your totals model.

A team on a 7-game winning streak gets inflated public betting action, moving the line against their actual edge. Use streak data to identify when the market has overreacted to momentum.

Compare both teams' last-5-game points allowed averages to build a dynamic total estimate. If one team has allowed 118+ in 4 of their last 5 while their season average is 112, the market total is likely stale.

If Cade Cunningham has scored 8+ points above his season average in each of his last 4 games, his points prop line is likely lagging behind his current form. Flag players 5+ points above or below their average and compare against posted prop lines.

Building a same-game parlay? Pull last-3 and last-5 rolling averages across all three categories. A player trending up across points, rebounds, and assists simultaneously is a more reliable parlay leg than one hot on just one stat.

Pull Cade Cunningham's stats with Scott Foster assigned. If he averages 28 points with Foster but 21 with other officials over a meaningful sample, that's a prop edge. The prop line is set on his season average — not his referee-specific average. That gap is the edge.

If a star player consistently scores below their average in Q1 when traveling to altitude, that's a first-half prop edge. Pull their altitude game quarter splits before betting first-half player props in Denver or Salt Lake City.