How to evaluate the effectiveness of a strategy in a long-term game

The effectiveness of the strategy over a long distance is not "lucky/unlucky in the evening," but the stability of indicators on many independent segments with unchanged rules. Below is a working frame that translates intuition into measurable metrics, replicable tests and honest conclusions.

1) First - goal and hypothesis

Define specific success criteria and horizon:

Goal: "minimize the 90th percentile of drawdown," "maximize the median result per 1000 spins," "increase the chance of finishing ≥0%."
Hypothesis: "Strategy A gives a slower result by ≥3 pp relative to strategy B on a batch of 1000 spins."
Horizon: Butch length (e.g. 1000 spin) and number of batches (minimum 30-50 for stable leads).

Important: if RTP is <100% and there is no external advantage, "efficiency" = a more acceptable risk profile (drawdowns, quantiles, chance of goals), rather than a miraculous change in expectation.

2) Correct "debt" metrics

1. EV per batch (average result in bets/%) - shows direction.

2. The median and quantiles of the result (Q50/Q75/Q90) are as "usual" and "bad" (the player lives in the median and tails).

3. Bank growth rate:

linear: average% per batch, log-growth (average 'ln (Bt/Bt − 1)'), relevant if the rate fraction depends on the bank.
4. Risk of ruin: the share of batches with bankruptcy/stop loss.
5. Max drawdown - median and 90th percentile.
6. Frequency of "significant events" (≥×10, bonus) and waiting intervals (median, 75th percentile) - for planning.
7. Stability over time: variance of metrics between batches, coefficient of variation.

Additionally, to compare strategies:

Sharp-like metric: average total/standard deviation of the total per batch.
Kelly-matching (if there is an edge): how much the selected bid share deviates from Kelly; penalty for under/over-measurement.

3) Design of the experiment: to make the conclusions honest

Butching: Divide the game into independent windows of equal length (e.g. 1000 spins each).

A/A tests: before A/B make sure that with the same strategy the system does not "see the difference" (false alarms).

Out-of-sample: setting rules on one set of batches, checking on another (no "rules that appeared after viewing all the data").

Common random numbers (CRNs) in simulations: Strategies are compared on the same noise.

Fixed exit rules: teik profit/stop loss, time out after L-streak - prescribed before the test.

4) Error and volume: how much "length" is needed

The standard batch average error decreases as (1/\sqrt {M}), where (M) is the number of batches. Landmarks:

30-50 batches ≈ minimally so that the median/quantiles become "recognizable."
For heavy tails (high volatility, rare large winnings) - 100 + batches.
To compare strategies by mean/median difference, use a bootstrap or permutation test, not just a t-test.

5) How to compare strategies (A vs B)

1. Batch metric (total%, max DD, chance ≥0%).

2. Difference (\Delta =\text {metric} _ A -\text {metric} _ B) for each batch (in pairs if CRN/paired batches).

3. Bootstrap 95% CI for (\Delta) and permutation test (p-value) - stable check without assumptions about normality.

4. Clinically relevant delta: Pre-set a threshold below which the difference is "not worth the complication of the strategy."

6) Shear and stability control

Long-term environment changes: RTP versions, provider pool, shares/cashback, spin speed.

CUSUM/control cards: watch the cumulative sum of deviations of the metric from its long-term average to notice drift.

Sliding windows: reports on the last 20-30 batches - early warning.

Stratification: Individual series by slot/volatility/stock time.

7) The money economy: Consider all

The effectiveness of the strategy is not only "backs." Include:

Cashback/rake-back/missions/tournament points: recalculate into "bets" or%.
Time/limit cost: longer sessions = higher exposure to tails.
Fees/currency conversion/provider limits: affect real EV and risk.

8) Kelly and growth rate (when there is an advantage)

If you have an external edge (real positive EV), the target metric is the average log growth of the bank.

The Kelly share maximizes log growth, but is aggressive; often use the "Kelly half" to reduce volatility.

With negative expectation, the optimal share is 0: "efficiency" is reduced to risk/pleasure management, not profit.

9) Long term traps

Retraining ("adjusted" the rules to history). Solution: out-of-sample and fixing the protocol in advance.

Multiple comparisons (testing dozens of strategies and choosing the "best"). Solution: adjustments (Bonferroni/FDR) or "league" with selection and validation.

Survivor displacement: see only "surviving" strategies. Keep history and do not hide closed ones.

Change of rate/slot in batch: breaks comparability.

Stopping "by luck": the test "to the first plus" distorts the distribution.

10) Mini evaluation protocol (can be inserted into the regulation)

1. Before the start: goal, metrics, batch length, number of batches, entry/exit rules, significance criterion, which is considered a success.

2. Collection: spin logs (bet, payout, ≥×10/bonus flags), batch results, max DD, duration.

3. Analytics: median and quantiles of totals, risk of ruin, waiting intervals, bootstrap CIs, permutation tests for A/B.

4. Stability: CUSUM, sliding windows, stratification.

5. Report: table of metrics, CI, conclusion "whether delta is significant enough," recommendations on the rate and limits.

6. Solution: "In production "/" Another 30 batches of data "/" Archive."

11) "Passport of strategy (long run)" - ready-made template

Strategy/Rule Version: .../...

Slot/Briefcase and RTP Pool:...

Batch: 1000 spins; butches:...

EV (batting average): ...% [95% CI... -...]

Median total (Q50 )/IQR: ... %/... -...%

Target chance: ≥0%...%; ≥+20%...%

Max drawdown: median... rates; 90th percentile...

Pre- ≥×10 intervals: median... spins; 75th percentile...

Risk of ruin per batch: ...%

Base comparison (flat): (\Delta) EV... pp [bootstrap DI... -...; p-permutations =...]

Stability: CUSUM - drift/no; sliding window - approx.

Cashback economy: +... p.p. to EV (calculation method -...).

Solution: implement/add/reject.

Notes: data limitations, environment changes.

12) A short checklist before the conclusion "strategy is effective"

Is there an out-of-sample confirmation?

Are CIs/quantiles/drawdowns shown, not just the average?

Are external bonuses/cashback counted?

Is the A/A test passed (the system does not "see" phantom deltas)?

Is there multiple testing without adjustments?

Does the strategy live on the same terms (RTP, rates, limits)?

Bottom line: long-term efficiency is about measurement discipline. Fix the goal, test on batches, compare strategies correctly (bootstrap, permutations, CRN), show not only the average, but also quantiles, drawdowns and risk. Take into account the cashback and drift of the environment, keep the protocol unchanged. So the strategy ceases to be a set of sensations and becomes a manageable tool with an understandable risk profile over a long distance.