# Charlotte vs Toronto > Major League Soccer · Kickoff Sat 16 May 2026, 23:30 UTC · [Canonical HTML](https://betsprinter.com/fixtures/35789) **Status:** Finished **Final score:** Charlotte 3–1 Toronto ## Model verdict - **Charlotte win:** 53% - **Draw:** 22% - **Toronto win:** 25% - **Source:** model ## Pre-match deep dive ### Model prefers home edge but markets hide several clear value lines ## The stage Major League Soccer action kicks off on Sat 16 May 2026, 23:30 UTC, with a fixture that carries routine regular-season significance rather than knockout finality[^fact-1]. The model gives the home side the lead in probabilities — Home 53% / Draw 22% / Away 25% — and flags that margin as robust, citing a 28 percentage-point gap to the runner-up probability[^fact-2]. Markets analysed against the model total three venues of comparison[^fact-11]. ## Form & momentum Recent form paints a picture of two teams trading inconsistencies. The home team’s last ten read LLWLW, equivalent to a 4-2-4 (W-D-L) split and 1.40 points per game, while averaging 1.80 goals scored and conceding 1.80 per match[^fact-4]. The visitors have a slightly more conservative recent record of LDDDW, or 3-4-3 (W-D-L), collecting 1.30 points per game with 1.70 goals scored and 1.90 conceded per match[^fact-5]. Elo places the home side ahead by 100 points after applying home advantage, a non-trivial edge in the system that helps explain the model’s leaning toward the hosts[^fact-3]. On balance, the home team’s form and the Elo differential suggest marginal control of the contest rather than dominance, with both sides conceding at roughly similar rates across the sample[^fact-4][^fact-5][^fact-3]. ## Personnel Key absences alter how each side can approach the game. The home side will be without Harry Toffolo, who is out injured[^fact-9]. The visitors will be missing Josh Sargent due to injury[^fact-10]. Both absences are named as the heaviest on the teams’ respective lists and will influence personnel choices and perhaps areas of tactical emphasis given each player’s typical positional importance[^fact-9][^fact-10]. No additional squad numbers or rotation plans have been supplied; analysis therefore has to measure impact through the available aggregate team-level stats and the stated absences[^fact-9][^fact-10]. ## Where the model sees value The model flags three specific market discrepancies where probability and market pricing diverge. - Away in Match Winner: the model assigns the away side a 37% chance while the market price of 4.85 at 10Bet implies a substantially lower probability, producing an edge of 16.7 percentage points and a high-confidence signal[^fact-6]. - Under 2.5 goals (Goals O/U 2.5): the model places 54% probability on under 2.5 while the market price of 2.20 at 888Sport implies a smaller implied probability, giving an 8.9 percentage-point edge with high confidence[^fact-7]. - No on Both Teams to Score: the model gives 49% to “No” while the market price of 2.30 at 888Sport implies a lower chance, leaving a 5.2 percentage-point edge at mid confidence[^fact-8]. These lines are derived from comparison across three markets analysed by the model and the market samples cited[^fact-11]. Note that the model’s top stance remains the host in the 1X2 set despite the away-side value in the Match Winner market; this is because the model’s 37% for the away win is being measured against market odds that the model judges soft, not because the model prefers the visitor outright in the 1X2 distribution where Home 53% sits on top[^fact-2][^fact-6]. ## Verdict The model’s lean is toward the home side — Home 53% — supported by a 100-point Elo edge with home advantage applied and home-side form that marginally outpoints the visitor over the recent sample[^fact-2][^fact-3][^fact-4][^fact-5]. However, market inefficiencies appear across three analysed markets: an away-match-winner price that the model treats as soft, and two lower-scoring signals (Under 2.5 and No on Both Teams to Score) where probabilities stack toward fewer goals[^fact-6][^fact-7][^fact-8][^fact-11]. These contrasts set up a classic mismatch between a model that favours the host overall and specific market prices that the model identifies as offering value away from the predicted 1X2 favourite[^fact-2][^fact-6][^fact-7][^fact-8]. ### Cited facts [^fact-1]: **Kickoff** — Sat 16 May 2026, 23:30 UTC — Major League Soccer [^fact-2]: **Model verdict** — Home 53% / Draw 22% / Away 25% (source: model; confidence high, 28 pp gap to runner-up). [^fact-3]: **Elo edge** — CHL vs TOR — Elo differential +100 points (with home advantage applied). [^fact-4]: **CHL recent form** — LLWLW last 10: 4-2-4 (W-D-L), 1.40 PPG, 1.80 goals scored / 1.80 conceded per match. [^fact-5]: **TOR recent form** — LDDDW last 10: 3-4-3 (W-D-L), 1.30 PPG, 1.70 goals scored / 1.90 conceded per match. [^fact-6]: **Value pick #1** — Away in Match Winner — model 37% vs market price 4.85 at 10Bet, edge 16.7 pp (high confidence). [^fact-7]: **Value pick #2** — Under in Goals O/U 2.5 — model 54% vs market price 2.20 at 888Sport, edge 8.9 pp (high confidence). [^fact-8]: **Value pick #3** — No in Both Teams to Score — model 49% vs market price 2.30 at 888Sport, edge 5.2 pp (mid confidence). [^fact-9]: **CHL key absence** — Harry Toffolo out (injury). [^fact-10]: **TOR key absence** — Josh Sargent  out (injury). [^fact-11]: **Markets analysed** — 3 market(s) compared against the model. --- Methodology: . Canonical HTML: .