A Mario Kart philosophy: Lag behind until you collect enough tools or weapons to reach the top fastest

In ranked Mario Kart races, strategies tend to be pretty aggressive. I found myself reaching sustainable effectiveness with one in particular.

I used to lose every race in which I spent most of the time in the top 5. So I started reaching those positions fast, on the 1st or 2nd lap, then deliberately lagging behind, drifting back naturally or playing indifferently, to stay between 8th and 15th place. Those positions maximize the chance of collecting the highest-potential items without requiring you to be in the very last places to get them.

This, plus the way racers bunch up in curves and certain parts of the map, particularly in the last lap, unlocks a significant advantage. So the strategy ends up being: stay in moderately low positions on purpose, waiting for compression, until you have farmed the right tools and figured out the exact time to use them: the last responsible moment.

If a skilled, competitive racer has the same base tools as the least competitive ones, they have a significantly higher chance of winning the race. This is basically economics.

Why it works

The item roulette is a catch-up mechanism: it hands stronger items to whoever is further behind, to keep the field close. But it reads rank, which the player chooses, instead of skill, which it can’t see. A skilled player can fake being a weak one and collect the transfer. In mechanism-design terms, the rule isn’t incentive compatible.¹

Three steps, left to right: lag, farm, fire late. The red arrow is the flaw the strategy exploits, the teal labels are the three conditions it needs, and blue is the outcome. Conceptual schematic.

Faking it only pays when three things hold at once:

Rank is free until the end. Only the final position counts, so 12th place on lap 2 costs nothing. NBA teams that lose on purpose for better draft picks exploit the same thing.²
Items pay most in skilled hands. The same item is worth more to whoever uses it better. Economists call this capital-skill complementarity,³ and it is why a transfer meant to help weak players widens the gap instead.
Falling behind is cheap. The pack bunches in curves and in the last lap, so the gap stays closable. It is how a cycling peloton reels in a breakaway that burned itself out in front.⁴

Timing is the last piece. A held item is an option, and firing it early throws away its value.⁵ So you wait for the last responsible moment,⁶ when the pack is tight and one item decides the race.

There is a ceiling. The very best players win from the front anyway: they know every curve and every drift, and build a lead that no item and no bunching can close. Lagging pays most for players who are skilled but not untouchable.

The general rule

Any catch-up mechanism that rewards a state instead of a type can be exploited by whoever controls their state cheaply. Remove any one of the three conditions and lagging stops working. Keep all three, and the system built to equalize the race ends up amplifying skill.

Society runs on the same mechanism. Means-tested state benefits are keyed to reported income, a state people can partly choose, rather than to the need they are meant to measure, so people position themselves in the bracket that pays the biggest premium. Chile’s Ficha de Protección Social, the score that decided eligibility for most benefits, placed 35% of the people it surveyed in its most vulnerable decile, against 22% of the national population. A 2010 expert committee traced the excess partly to households answering in whatever way lowered their score.⁷

References

Myerson, R. B. (1979). Incentive compatibility and the bargaining problem. Econometrica, 47(1), 61–73. doi:10.2307/1912346. ↩
Taylor, B. A., & Trogdon, J. G. (2002). Losing to win: Tournament incentives in the National Basketball Association. Journal of Labor Economics, 20(1), 23–41. doi:10.1086/323930. ↩
Griliches, Z. (1969). Capital-skill complementarity. The Review of Economics and Statistics, 51(4), 465–468. doi:10.2307/1926439. ↩
Olds, T. (1998). The mathematics of breaking away and chasing in cycling. European Journal of Applied Physiology, 77(6), 492–497. doi:10.1007/s004210050365. ↩

Why it works

The general rule

References

Footnotes