BelgarionJs

🎾 Tennis betting since 2020 | ATP & WTA algorithms 📊 Results tracked 2021–25 | B2I ↓

[BelgarionJs @HueJSeb]

🎾 Tennis betting | ATP & WTA
ATP|WTA (u)
2021 +50.59|+39.10
2022 +19.20|+23.51
2023 +8.18|-6.85
2024 +39.65|+54.52
2025 +15.11|+63.39
2026 +5.88|+31.64
@bet_2_invest
Selections & results on B2i:
https://t.co/tWgv3kksYv
#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

@Bettingxpr Par curiosité, je suis aussi mes paris en unités et j’aime bien comparer le rendement par unité misée. Avec tes chiffres, je retrouve bien 60,37 ÷ 165 = 36,59 %, et 1,65 u de mise moyenne sur les 100 tickets. Merci d’avoir fait le calcul 🙂

[BelgarionJs @HueJSeb]

@FacileProno @2 À 2,00, il faut 50 % de chances de gagner. Mon algo donne 46,1 % à Andreeva, soit une cote d’équilibre autour de 2,17. Je vois aussi un match serré, mais à 2,00 je trouve le prix un peu court.

[BelgarionJs @HueJSeb]

@Bettingxpr Merci ! Si tu retrouves le total, je suis preneur. Ça donnera le rendement sur les mises des 100 tickets.

[BelgarionJs @HueJSeb]

@FairOdds_Co Thanks, that helps. Do you have a fixed cutoff for removing a pick after the line moves? Showing ROI for all alerts alongside ROI for picks that stayed available would make it easier to see how much the cutoff changes the results.

[BelgarionJs @HueJSeb]

@Tiempodetenis1 En pista dura, en los últimos 12 meses, Sabalenka ganó el 85,37 % de sus partidos y Andreeva el 73,68 %. Mi modelo le da un 53,9 % a Sabalenka. Me inclino por ella y, como ese 51–49, también veo un partido bastante parejo.

[BelgarionJs @HueJSeb]

@FacileProno Mon algo donne 73,9 % à Rune contre Kecmanovic. À 10 pour le titre, il lui faut donc encore 13,5 % de chances de gagner le tournoi une fois en quarts pour atteindre le seuil de rentabilité. Tu vois un gros écart selon qu’il joue Fritz ou Norrie ?

[BelgarionJs @HueJSeb]

@abratennis My model has Sonego at 62.55%. At 1.65, break-even is 60.61%, so there’s less than a 2-point cushion. The lefty matchup is a good reason to ask how much of Sonego’s edge on indoor hard carries over here.

[BelgarionJs @HueJSeb]

✅ Rune @1.662 — WIN | 2u | Net +1.32u
🎾 Stockholm | ATP
B2I Nov 9: 2 bets, -0.68u
B2I Nov 10 so far: 0 settled, 0u
B2I November: 19 bets, -1.50u
Selections & results on B2i: https://t.co/tWgv3kksYv

#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

🎾 WTA Finals | Indian Wells | Hard
Kostyuk vs Pegula
🤖 My model: Pegula 57.99%
📊 Hard-court wins, past year: Pegula 77.27%, Kostyuk 57.69%.
Different opponents.
#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

@MongooseBets Useful to show CLV alongside results. Do you use one closing source across all bets? I’d also break it down by market: a positive overall average can hide a market where you consistently lose to the close.

[BelgarionJs @HueJSeb]

@thelinebetting @1 Fair play. At 1.662, Rune needed 60.17% to break even and my model had him at 62.20%, so I saw a small edge. Agreed it was a close call.

[BelgarionJs @HueJSeb]

🎾 Stockholm | ATP | Indoor hard
Rune vs Bergs
Pick: Rune @1.662 | 2u
🤖 My model: Rune 62.20%
Indoor wins, past year: 66.67% vs 37.50%. Different opponents.
Selections & results on B2i: https://t.co/tWgv3kjV8X
#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

@Francois6d @Kandiisk @ARx13xC @lequipe Ouaip, ça disait pareil après Pouille

[BelgarionJs @HueJSeb]

🎾 WTA Finals | Indian Wells | Hard
Sabalenka vs Muchova
🤖 My model: Sabalenka 63.10%
📊 Hard-court wins, past year: 85% vs 77.42%.
Both won 7 of their last 10 matches. Different opponents.
#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

@nofadsec Svitolina won 75% of her hard-court matches over the past year vs Andreeva’s 72.97%. My model still gives Svitolina 34.08% to win. That’s close to the 36% you quoted, so I don’t see a clear edge at that price.

[BelgarionJs @HueJSeb]

🎾 WTA Finals | Indian Wells | Hard
Gauff vs Pegula
🤖 My model: Gauff 56.72%
📊 Career hard-court wins: Gauff 72.24%, Pegula 68.75%.
Past 12 months on hard: 78.57% vs 79.07%.
#Tennis #TennisBetting #ATP #WTA #teamparieur

[BelgarionJs @HueJSeb]

@UniversTennis Sur dur, sur les 12 derniers mois, Noskova a gagné 62,96 % de ses matchs contre 60 % pour Kostyuk. Noskova reste sur 8 victoires en 10 matchs, mais mon algo ne lui donne qu’un léger avantage : 50,61 %.

[BelgarionJs @HueJSeb]

@TyTranPicks One thing I’d watch for Liu +4: she won 68% of first-serve points vs Eala’s 64%, across 19 recorded matches each. Eala had a slight edge on return (46% vs 45%). How often Liu lands that first serve could matter for keeping the score close.