Auction Price and Overs Debt: Workload Depreciation in the T20 Market

A Death-overs specialist went for seven times his base price at the last auction. The room applauded. I did not, because my laptop was running a query: how many overs this bowler had sent down in fourteen months, and how fast his economy in the last two overs was shifting. The number was not shocking. The gap between the price and the work was. People spend auction night telling stories about money; I spend it counting overs debt.

I am Mehedi Ahmed, twenty-five, based in Singapore, working as a sports data analyst, covering cricket for the Singapore market. I began writing reports from a spreadsheet. The spreadsheet was my cloister; the World Cup was my first pilgrimage. That spreadsheet gave me a habit—look at a player's load before praising him, look at the arithmetic behind a price before trusting it. This habit is not arrogance; it is survival. In a transfer window, a mispriced asset is the biggest story, and it is the story everyone misses.

Right now the cricket economy sits inside a vast transfer cycle. T20 league auctions, retention lists, right-to-match cards, deferred payments, the power struggle between boards and franchises—together, a market. Here a player is an asset, and asset prices are set by demand and narrative, not by accumulating evidence. At the auction in Dubai on December 19, 2026, Mitchell Starc's price reached INR 24.75 crore and Pat Cummins' INR 20.5 crore—those numbers are not the product of any franchise's internal load accounting. They are the product of an auction room's emotion. Sam Curran went for INR 18.5 crore in the 2026 auction; that price tells the same story.

My real problem starts here. Trying to build an xG-style model for cricket, I hit a wall immediately. Football's xG works because shot location, angle and defender pressure are relatively constant. In cricket, chance quality is fractured across so many layers that transplanting the model directly yields wrong answers. Within a single over the pitch changes, dew falls, field settings shift, the bowler's line shifts. So I do not transplant football's model into cricket. I build cricket-native measures: expected runs per over, six-hitting probability in the death, and most importantly—the overs debt carried on the shoulder.

This debt is simple to compute, yet nobody does it. For every bowler I take a rolling fourteen-month window. I look at how many matches he played, how many overs he bowled, how many kilometres he travelled, and how much his pace and line-length degrade after each spell. A speed gun is not always available, but broadcast tracking data now exists in almost every major league. With that data a simple depreciation curve can be drawn.

A bowler's pace and accuracy fall on a depreciation curve exactly the way a car's value falls as mileage climbs—the only difference is that the franchise's books never record that depreciation. Yet the price is set in ignorance of that unrecorded depreciation. Take an example. Suppose a young pacer has bowled more than 240 overs across domestic and franchise cricket in two seasons, a third of them in the death. His average pace in his first ten overs was 140 kph; in his last ten it dropped to 135, and his yorker accuracy fell six percentage points. The model suggests a risk that over the next six months his death-over economy drifts from 8.2 toward roughly 11. Yet his auction price is rising, because scouts are watching pace, watching wickets, watching one best spell—not watching the overs balance sheet.

From a small flat in Singapore I watch three time zones at once. Australia's league in the morning, an Asian tournament at noon, Africa or the Caribbean at night. Watching three screens together reveals something a single match never shows—the same bowler, in the same week, bowling at two different paces on two continents. The human eye cannot catch this difference, because the eye watches match by match. A model watches player by player. My entire job stands on that gap.

But here I must stop, and the stopping is the most important part of my writing.

A bowler's performance declining does not mean his workload is to blame—jumping to that conclusion is the easiest mistake. It is the trap between correlation and causation. Is a bowler's economy rising because he is tired, or because he was cast in a different role that season—not with the new ball, but in the death? Or because the pitch has dried out, or because dew is making the ball slippery? When all four causes occur together, the data shows the same curve, but the remedy is entirely different. A tired bowler needs rest; a mis-cast bowler needs his role changed. If a model cannot separate the two, using it to make decisions worth crores is dangerous.

So I never write a conclusion off one match. I pre-specify the comparison conditions, then look at the data. In a small project last year I studied the empty stadiums of several post-pandemic cricket tournaments, because that was a natural experiment—an external shock no one planned. I measured the ghost games, then measured what they did to legs. Home advantage fell in crowdless grounds, but pace bowlers' workload management also shifted, because play ran on a compressed calendar. When two effects arrive together, the real question becomes: which one are we measuring? For me, empty stadiums taught me that silence is a variable, not an absence. But I do not stop at making silence a variable; some things never enter the model at all, and for those I keep a separate room.

That separate room is the player's own voice. A workload model yields a number, but behind that number is a person with his own language of fatigue, a family, pressure, and an opinion about his own body. When I call a bowler an 'undervalued $45m asset,' I should be careful—because an asset has no voice, and a player does. This caution does not weaken my model; it makes it honest.

Still, back to the market, because the market does not wait. When a franchise buys a bowler at a record price, it is really buying future overs—overs he has not yet bowled, but whose quality is already heading into depreciation. It is rather like buying a bond: you buy a promise of a certain yield, but the longer the bond's maturity, the larger the interest-rate risk. For experienced bowlers like Starc or Cummins the maturity is short, the risk limited, so the price is defensible. For a young pacer the maturity is long, and that long-maturity risk is written in no scouting report. This is where price and value diverge, and that divergence is the market's inefficiency.

A franchise's portfolio should really be split into three distinct assets: a bowler who can bowl overs now, a bowler who can bowl overs six months later, and a bowler who will bowl more overs in a year if he is rested. In practice franchises keep all three in one box, and in one auction room's emotion all their prices rise together. This is why smaller-budget teams often make the best trades—because their buying power is limited, so they must do the arithmetic, and the luxury of narrative is denied them.

Now to the place where this whole discussion is most likely to go wrong. If I say a bowler's economy is rising, therefore he is tired, then I am taking one match or one spell as proof—which is not a model, only an anecdote. One innings, one injury, one empty stand is never proof on its own; it is a signal needing replication, needing caveats. My job is to measure that signal with suspicion, with humility, and where the model falls silent, to be willing to stay silent.

Even so, one direction is clear: the biggest price inflation at the next auction will be for those bowlers whose overs count the fewest people have bothered to check. When franchises set prices by ignoring the fourteen-month balance sheet and watching only pace and one great spell, a blank cell accumulates in my ledger—a cell that will fill in next season's death overs, when someone will call it bad luck, and I will say the depreciation landed right on time. The most valuable asset in the cricket market is not pace; it is human durability—and durability has no spot price, only a depreciation schedule.

So at the next window I will watch one thing, and it is not any star—it is which franchise first adds a column called 'overs debt' to its scouting report. The team that does it first will buy cheaply before everyone else over the next three seasons, because it is measuring something others still mistake for a story.