Meet Our Analysts

Q: What is your approach to match prediction?

I always start with the pitch. People underestimate how dramatically a surface can change over the course of a tournament. A fresh Wankhede pitch in late March will offer true bounce and carry, so seamers get some assistance early, but by the time IPL moves into May, that same strip has been used three or four times and it slows down considerably. Spinners start getting grip, and the ball stays low. If you are predicting a match at the same venue four weeks apart and treating the conditions the same way, you are going to get it wrong.

After pitch conditions, I look at toss dynamics — not just who wins the toss, but what the smart choice actually is at that venue at that point in the season. Then I layer in recent form, paying close attention to the last three or four innings rather than season-long averages. A batter who has scored 20, 15, 8 in his last three is in trouble regardless of what his season average says.

Q: Which IPL 2026 team are you most excited about?

I am keeping a close eye on Rajasthan Royals this season. They have consistently invested in scouting lesser-known domestic talent, and their support staff is among the best in the tournament at optimizing player roles. Their approach to the powerplay has evolved — they are not just trying to survive it with the bat anymore, they are being proactive. The way they used the auction to build bowling depth at the death tells me they have learned from past playoff exits where their fifth and sixth bowling options got exposed.

That said, you can never count out Chennai. They have this institutional memory that other franchises lack. Their understanding of Chepauk, how to set up totals there, when to use the slower ball into the pitch — it is almost second nature to their core group now.

Q: What is the single biggest factor most fans overlook?

Dew. Without question. In evening matches across most Indian venues, dew starts settling around the 14th or 15th over of the second innings. Bowling with a wet ball is a nightmare — your cutters do not grip, your yorkers become full tosses, and even experienced death bowlers lose control. This is why chasing teams at certain venues win 60 to 65 percent of the time. I factor dew probability into every single prediction for evening games, and I would say it flips the outcome in at least five to six matches every season where the team batting first put up what looked like a competitive total.

Q: How has IPL evolved in recent years?

The biggest shift I have seen is in powerplay bowling. Five or six years ago, teams would just try to contain in the first six overs — keep it to under 50 and you were happy. Now, the best teams are using the powerplay aggressively with the ball. They are attacking with short balls, using the new ball swing, and actually trying to take wickets rather than just restricting runs. Teams like Gujarat and Mumbai have embraced this philosophy, and it has changed how batters approach those first six overs. You see a lot more caution from openers now compared to 2022 or 2023.

The other evolution is squad management. With the Impact Player rule, teams are essentially playing twelve vs twelve. Coaches who know how to use that tactical substitution — bringing in an extra batter or bowler based on conditions — are gaining a real edge. It has added another layer of complexity to predictions because you have to anticipate not just the playing XI but also the Impact Player choice.

Q: What is the biggest mistake fantasy cricket players make?

Chasing past performance without considering context. I see so many players pick a batter as captain just because he scored a century last week. But was that century on a flat Chinnaswamy pitch against a depleted bowling attack? If the next match is at Eden Gardens on a seaming track against a completely different bowling unit, that context changes everything.

The other mistake is ignoring ownership percentages. In large pool contests, you cannot win by picking the same team as 80% of other players. You need calculated differentials. I am not saying pick random players — I am saying find the player who is likely to perform well but is only in 10 to 15 percent of teams. That is where your edge comes from. A player at 12% ownership who scores 50 fantasy points is worth far more in a mega contest than a player at 70% ownership who scores 80.

Q: How do you decide on captain and vice-captain picks?

Captain selection is the single most important decision in Dream11. Your captain gets 2x points, so getting this right or wrong can swing your total by 60 to 80 points. My process is fairly systematic. First, I identify the top five or six players most likely to have a high-scoring game based on matchup, venue, and batting position. Then I cross-reference with expected ownership — if one of those players is going to be captained by 30% or more of the pool, I look for someone from that same shortlist who is equally likely to perform but at much lower ownership.

For vice-captain, I tend to pick all-rounders whenever possible. The reason is simple: they have two avenues to score points. Even if they have a quiet day with the bat, four overs of tight bowling can still return 40 to 50 fantasy points. Players who both bat in the top six and bowl their full quota are gold in fantasy cricket.

Q: Which IPL 2026 team are you most excited about from a fantasy perspective?

Punjab Kings are fascinating for fantasy this year. They tend to play on high-scoring pitches, and their batting lineup is stacked with aggressive stroke-makers. That means even their number five and six batters can score quickly and return fantasy points through boundaries and strike rate bonuses. In Dream11, you want players from teams that are likely to be involved in high-scoring matches because more runs and more wickets mean more fantasy points on the table for everyone.

On the flip side, I also watch for value picks from teams like Lucknow Super Giants. Their Indian quicks are often priced low in credits but bowl at the death where wickets and dot balls generate serious fantasy returns. Finding a bowler priced at 8 credits who consistently delivers in the death overs is one of the best ways to free up credits for a premium batter.

Q: How do you handle uncertainty on match day — like late team changes?

This is where preparation matters more than reaction. I always build two or three team variants before lineups are announced. One version assumes the expected XI, another accounts for the most likely change — maybe a spinner coming in for a pacer on a turning track. When the actual XI drops, usually 30 to 45 minutes before the deadline, I can switch to the right variant quickly instead of scrambling.

The toss is the other variable. If a team wins the toss and bowls first on a venue where batting second is statistically favored, I may shift my captain to someone from the chasing side. Having those contingency plans ready is what separates consistent fantasy performers from people who panic-edit their team in the last five minutes.

Q: What is your approach to match prediction from a data standpoint?

The model works in layers. The base layer is venue history — average first innings score, win percentage batting first versus second, average powerplay run rate, and death-over economy. These numbers are surprisingly stable over time and give you a solid baseline for what to expect at a given ground.

The second layer is team-specific form weighted heavily toward the last 10 matches. I use exponential decay weighting, so a match played last week has roughly three times the influence of a match played six weeks ago. Then comes the player matchup layer — I track batter-versus-bowler records, but only when the sample size is above 20 deliveries. Anything less and you are looking at noise, not signal. The final layer is contextual adjustment: toss outcome, time of match, tournament phase. Teams play differently in must-win games versus dead rubbers, and the model accounts for that.

Q: Which statistical metric do you think is most underrated in cricket analysis?

Death-over economy rate, by a wide margin. Everyone looks at a bowler's overall economy, but the split between overs one through fifteen and overs sixteen through twenty tells you a completely different story. A bowler with an overall economy of 8.5 might be bowling at 7.2 in the middle overs and 11.0 at the death — and those are two entirely different bowlers from a prediction standpoint.

In T20 cricket, the death overs disproportionately determine the outcome. My data shows that the team with the better death bowling economy wins approximately 62% of IPL matches. Compare that with powerplay performance, which correlates with winning only about 54% of the time. So when I am evaluating team strength, I weight death bowling ability much more heavily than most surface-level analyses would suggest.

Q: How accurate can prediction models really be for cricket?

I think people need to have realistic expectations here. Cricket has more variance than almost any other sport. A single dropped catch, a misjudged DRS review, or a freak runout can swing a match completely. Over a large enough sample, the better team wins — but in any individual T20, the underdog has roughly a 35 to 40 percent chance regardless of how strong the favorite is.

Our model targets 65 to 70 percent accuracy across a full season. That might not sound incredible, but consider that random prediction gives you 50%. Bookmaker odds — which factor in millions of dollars of market intelligence — typically convert to around 68 to 72 percent accuracy for the favorite. So if we are consistently in that 65 to 70 percent range using public data and domain expertise, we are operating close to the practical ceiling for pre-toss prediction. Post-toss, accuracy jumps by about 5 to 7 percentage points because the toss outcome removes a significant variable.

Q: What data trend are you watching closely for IPL 2026?

The increasing effectiveness of wrist spinners in the middle overs is a trend worth tracking. Over the last three IPL seasons, leg spinners and wrist spinners have seen their middle-over economy rates drop from about 8.1 to 7.4, while their wicket-taking frequency has gone up. This is partly because batters have become so aggressive against finger spinners that teams are using wrist spin as their primary attacking option in overs seven through fourteen.

For IPL 2026, I am also looking at how teams use their overseas slots. The auction dynamics this cycle forced some franchises to invest heavily in Indian fast bowlers, which means their overseas picks are leaning more toward batting all-rounders. That shift in squad construction affects the balance of the playing XI and creates interesting matchup asymmetries that the model can exploit.

The process begins 48 hours before each match. Vikram's model ingests venue history (average scores, phase-wise run rates, boundary dimensions), team form (last 10 matches with exponential decay weighting), player matchup data (batter vs bowler records with a minimum 20-ball sample), and head-to-head records. The model outputs a raw win probability for each team along with projected score ranges for both innings.

Arjun reviews the raw model output and adjusts based on factors that data alone cannot capture: current pitch condition from on-ground reports, weather and dew probability for evening matches, team news and injury updates, tactical patterns from recent games (such as a team's tendency to play an extra spinner when returning to a specific venue), and tournament-phase psychology. These adjustments typically move the win probability by 3 to 8 percentage points.

Priya takes the finalized match analysis and translates it into actionable Dream11 recommendations. She identifies which players are best positioned to score fantasy points given the predicted conditions, determines optimal captain and vice-captain choices by cross-referencing performance ceiling with expected ownership percentage, and highlights differential picks that offer high upside at low ownership risk.

After the toss, we publish a rapid update adjusting our prediction based on the toss outcome and confirmed playing XIs. This update is typically published within 15 minutes of the toss and includes revised win probability, any changes to Dream11 recommendations based on the actual XI, and Impact Player expectations based on conditions and team composition.