0.5 baseline — 0.6 × season 0.4 (21 gm) + 0.4 × last 10 0.8
−0.0 opponent leak — they concede 22.6/game vs a 23.7 league average
−0.2 at this ground — averages 0.4 there (62 gm)
−0.3 wet forecast — averages 0.3 in the wet (15 gm)
vs them: averages 0.6 over 13 games — evidence, never summed
SignalsL10 form ▼opponent ▼venue ▼wet ▼projection ·
Most signals here point under while you're on the over — worth reading the working above before you commit.
Derived, not measured — plain arithmetic over his real games, shown in full. Adjustments can overlap (a game at this ground can also be a wet one); they add simply, and this note is the admission. Round 13: 4 — far outside his usual range; kept in the average, not smoothed away. Round 17: 4 — far outside his usual range; kept in the average, not smoothed away.
Line Over Under
Tap a bar for full game detail · newest on the right
Player percentile is among everyone with 5+ games this season. Opponent percentile is among the 18 clubs — p100 means they concede more of this stat than any other side.
NEW concede · Points
22.6per game · league avg 23.7
#11 of 17 most conceded
Middle of the road matchup.
Venue split · Points
At GIO Stadium0.4(62)
Everywhere else0.4(74)
Since 2024, all stats real.
Wet vs dry · Points
Wet games (≥1mm)0.3(15)-0.1
Dry games0.4(121)
Weather backfilled from station data at each venue.
Similar players vs NEW · Points
Avg diff
+0.3
Avg diff %
+30%
Hit rate
2/8
beat their own average
DateTeamPlayerAvgResultDiff
08-09CBRCorey Horsburgh0.40-100%
08-09CBRAta Mariota0.000%
08-09CBRMatthew Timoko1.40-100%
08-09CBREthan Strange1.30-100%
08-09CBRJoseph Tapine0.000%
08-09CBRZac Hosking1.14+280%
08-09CBRJed Stuart1.14+260%
08-09CBRMorgan Smithies0.000%
Similar = season average within ±1.5 of Tom's. Computed from this app's own records.