Model hypothetical tactical scenarios — key absences, formation shifts, opposition style — and translate them into data-backed betting and FPL decisions using KiqIQ AI and the Poisson Calculator.
Pick a fixture profile and a key absent player. The simulator recomputes the Poisson model in real time and shows the probability shift across 1X2, Over 2.5, and BTTS markets.
Markets are efficient at processing known information — the season xG table, league position, head-to-head records. Where inefficiencies emerge is in the processing of qualitative tactical context: how does a team change when their best player is absent? How does their xG shift against a high-press opponent versus a deep block?
Bookmakers price these scenarios imperfectly. The crowd of recreational bettors uses reputation and results — not adjusted xG models — to form opinions. A rigorous what-if analysis gives you a probability estimate that diverges from the market, and divergence is where edge lives.
Each scenario type has a core question, a research approach using KiqIQ, and direct betting and FPL applications.
What does Arsenal's xG look like without Saka?
Research Approach
Ask KiqIQ for the team's xG in the last 5 games with and without the player. Identify who fills the role and their average output. Adjust the Poisson model inputs accordingly.
Betting Application
If the absence drops projected xG from 1.8 to 1.3, the team's win probability falls — price may not reflect this yet if injury news broke late
FPL Application
Eliminates the player from your captain consideration and may open a differential opportunity for whoever deputises
AI Prompt
"What is Arsenal's xG per game in matches where Saka started vs. matches where he didn't start this season? Who typically plays in his position when he's absent and what is their average xG contribution?"
If Man City shift from 4-3-3 to 4-4-2, how does that change their xG profile?
Research Approach
Ask KiqIQ about the team's historical xG and xGA in different formations. Cross-reference with press stats (PPDA) to understand the defensive trade-offs. Then model the adjusted scenario in the Poisson calculator.
Betting Application
Formation changes often shift the balance between attacking output and defensive solidity — valuable for over/under goals and BTTS markets
FPL Application
A shift to 4-4-2 may unlock a striker's haul potential or reduce the assists from midfield — affects captain and transfer decisions
AI Prompt
"When [Team] has lined up in a [formation] vs. their usual [formation] this season, how has their xG per game changed? How has their xGA and pressing intensity (PPDA) changed in each setup?"
How does Chelsea play differently against high-press teams vs. low-block defences?
Research Approach
Ask KiqIQ for the team's xG per game split by opponent PPDA bracket (high press vs. low block opponents). This shows which tactical style the team performs better against.
Betting Application
A team that struggles against high-press may be overpriced against a high-energy opponent despite being the "home favourite"
FPL Application
Identifies which fixtures will generate more or fewer attacking returns for your FPL assets — key for captain timing and wildcard planning
AI Prompt
"Split [Team]'s xG per game this season into two groups: games against teams with PPDA under 8 (high press) and games against teams with PPDA above 10 (low block). How does their attacking output and defensive record differ between these two tactical environments?"
Is [Team]'s home advantage as strong as the odds suggest?
Research Approach
Compare xG home vs. away for both teams. Many teams have stronger or weaker home effects than the market prices in — especially after venue changes, COVID-era adjustments, or new managers.
Betting Application
If the home team's home xG average is 1.4 but the odds price them as if it's 1.8, there is a clear discrepancy worth modelling
FPL Application
Away fixtures at strong grounds may suppress clean sheet probability even for reliable defenders — home/away xGA split is more accurate than season averages
AI Prompt
"Compare [Team]'s xG per game at home vs. away this season. Also compare their xGA at home vs. away. How significant is their home advantage in data terms, and does the difference hold for their last 10 home games specifically?"
How does the backup goalkeeper's save percentage affect our clean sheet probability?
Research Approach
Ask KiqIQ for the first-choice keeper's PSxG (Post-Shot xG) and save percentage vs. the backup. Then recalculate the clean sheet probability with adjusted goalkeeper inputs.
Betting Application
A backup keeper with significantly lower save percentage increases opponent goal probability — affects both BTTS and clean sheet odds
FPL Application
FPL defenders behind a backup keeper have lower clean sheet probability — move budget away from that defence
AI Prompt
"What is [Team]'s first-choice goalkeeper's save percentage and PSxG-GA this season? If [backup keeper] plays instead, what do we know about their save rate and how might that affect the team's expected goals conceded?"
Does heavy rain affect this team's passing-based xG model?
Research Approach
Ask KiqIQ about the team's xG performance in wet/heavy conditions vs. dry. Some possession-based teams see significant xG reduction in adverse conditions as passing accuracy drops and long balls increase.
Betting Application
Under/under goals markets and BTTS can be affected by pitch conditions — especially for sides who rely on intricate build-up play
FPL Application
Heavy conditions reduce chance of multiple goalscorers; more relevant for defenders and goalkeepers who benefit from clean sheet probability
AI Prompt
"Has [Team]'s xG or goal output per game differed significantly in wet/heavy weather conditions vs. dry this season or last? Are there any notable patterns in their defensive record in adverse conditions?"
This is the complete process from defining your hypothesis to sizing the bet — consistently applied to every tactical scenario you model.
State clearly what tactical change you are modelling and why it matters. Example: "Arsenal without Saka vs Wolves — does this change the match probability enough to affect my bet?"
Get the current season xG and xGA averages for both teams — your baseline model inputs. These go straight into the Poisson calculator.
Use KiqIQ to research the specific scenario (player absence, formation change, etc.) and estimate the xG delta. Typical adjustments: key striker absence = -0.2 to -0.4 xG per game.
Open the Poisson Calculator and run two models: one with base xG inputs and one with adjusted inputs. Compare the win/draw/loss probability outputs side by side.
Convert the adjusted Poisson probabilities to implied prices using the Implied Probability calculator. If the bookmaker's price implies a higher probability than your adjusted model, pass on the bet. If lower, you may have value.
Your tactical what-if scenario has either confirmed or challenged the market price. Size the bet using Kelly Criterion if the edge is confirmed.
Hypothesis
Saka is injured and misses Arsenal vs Wolves (home). Arsenal are priced at 1.45 to win. Does the absence change this enough to pass on the bet?
Base Model (with Saka)
Arsenal home xG average: 1.85
Wolves away xGA average: 1.60
Poisson win probability: 62%
Fair price: 1.61
Adjusted Model (without Saka)
KiqIQ: Arsenal xG drops ~0.25 without Saka
Adjusted Arsenal xG: 1.60
Adjusted Poisson win %: 55%
Adjusted fair price: 1.82
Decision: Pass on Arsenal 1.45 to win
The market is pricing Arsenal at 1.45 (68.9% implied) but our adjusted model gives them 55% — a significant mismatch. The absence has not been fully priced in. Backing Arsenal at 1.45 is negative EV under this model.
xG adjustments for player absences are estimates based on historical data — they carry uncertainty. The goal is not precision but direction: if the absence reduces projected xG from 1.8 to 1.4, that's a significant signal even if the exact number is 1.3 or 1.5 in reality. Use adjustments directionally, not as exact outputs.
Absolutely — tactical what-ifs are at least as useful for FPL as for betting. Key player absences, formation shifts, and favourable fixture matchups all affect expected returns. The differential scout workflow and matchday AI playbook both build on the same xG adjustment logic.
Compare opening odds (set before the news) to current odds. If a key player's injury was announced and the odds have moved significantly, the market has adjusted. If they have not moved, the news has not been absorbed — this is where tactical what-if analysis finds its biggest edges.
Pick a fixture profile and a key absent player. See the probability shift across 1X2 and Over/Under markets.
| Outcome | Baseline | Adjusted | Shift |
|---|---|---|---|
| Home win | 58.4% | 40.5% | -17.8pp |
| Draw | 22.6% | 28.9% | +6.4pp |
| Away win | 19.1% | 30.5% | +11.5pp |
| Over 2.5 goals | 52.8% | 37.7% | -15.1pp |
| BTTS Yes | 51.5% | 44.2% | -7.3pp |
Why this shift: City's xG drops ~25% in fixtures without Haaland. Replacement (Alvarez/young striker) finishes lower-quality chances.
For informational and entertainment purposes only. Probability shifts are conservative estimates from historical with/without xG patterns. Actual fixture outcomes depend on many factors not modelled here. When live API-Football data and the Poisson model service are configured, this simulator pipes them in automatically — the UI is unchanged either way.
