Collectibles, gaming & sports analytics kernels

Museum conservation and collections-management arithmetic, coin valuation and grading, lottery combinatorics and expected-value math, and sports performance metrics, across four independent scalar-for

Four modules for domains that share nothing structurally except that each reduces a real-world judgment call to a small formula: how much daylight a fragile object can survive, what a coin is worth beyond its metal content, whether a lottery ticket is ever a rational bet, and how to compare a .300 hitter to a 25-point scorer. museum and numismatics are both valuation-and-care math for physical collections — one for institutional holdings, one for a single graded coin — while lottery and sports_analytics are both probability-and-scoring math for games, one for pure chance and one for measured skill. None of the four calls into any of the others; they are grouped here because each is too small to justify its own page, not because a museum curator and a sportsbook share any code path.

from algenta import MojoRuntime

runtime = MojoRuntime(mode="local")

# the number of distinct 6-of-49 combinations -- a standard "6/49" lottery's jackpot
# odds are exactly 1 in this number
runtime.execute("lottery", "combinations", [49, 6])["result"]
# 13983816.0

# a $2 ticket with a $0.50 expected return keeps 75% of every dollar wagered
runtime.execute("lottery", "house_edge", [0.5, 2.0])["result"]
# 75.0

The modules

Module	What it does
museum	Conservation-science formulas for light exposure, humidity/moisture equilibrium, preservation indices, and climate stability, plus collections-management ratios: storage density, visitor density, growth rate, insurance appreciation, and a weighted conservation-priority score
numismatics	Coin valuation and grading arithmetic: melt value, premium over melt, Sheldon-scale grading, population-report scarcity, annualized return, auction fees and total acquisition cost, and bullion content in troy ounces
lottery	Combinatorics (C(n,k), P(n,k)), lottery odds, expected value and house edge, probability of at least one win across multiple tickets, annuity-vs-lump-sum present value, and post-tax payout math
sports_analytics	Baseball rate stats (batting average, OBP, SLG, OPS, ERA, WHIP), a simplified NFL passer rating, Pythagorean win expectation, Elo expected score and rating update, and basketball true-shooting-percentage and PER approximations

lottery: combinatorics, odds, and whether a ticket is ever +EV

Function	What it does
combinations(n, k)	C(n,k) = n! / (k!(n-k)!), computed by an iterative running product (never materializes a factorial), which is what keeps combinations(49, 6) from overflowing; 0.0 if k < 0 or k > n
permutations(n, k)	P(n,k) = n! / (n-k)!, same iterative shape; 0.0 if k < 0 or k > n
lottery_odds(total_numbers, drawn, bonus)	C(total, drawn) * bonus — the 1-in-X odds of matching every drawn number plus landing the bonus ball; pass bonus = 1 for a game with no bonus draw
expected_value(prize, probability)	prize * probability
expected_value_net(prizes_ev_sum, ticket_cost)	Σ(EV) - ticket_cost; the caller sums each prize tier's own expected_value first and hands in the total
house_edge(expected_return, ticket_cost)	(cost - return) / cost * 100; 0.0 if ticket_cost is within 1e-30 of zero
probability_at_least_one_win(p_single, n_tickets)	1 - (1-p)^n, evaluated as 1 - exp(n * log(q)) rather than repeated multiplication, so it stays accurate for the large ticket counts a real EV analysis needs; 0.0 if n_tickets <= 0
annuity_vs_lump_sum(annual_payment, years, discount_rate)	Present value of a fixed-payment annuity, payment * (1 - (1+r)^-n) / r; falls back to payment * years when discount_rate is within 1e-30 of zero, avoiding the 0/0 the closed form would otherwise hit
tax_after_winnings(gross, federal_rate, state_rate)	gross * (1 - federal - state)
odds_to_probability(odds_against)	1 / (odds + 1) — converts "9-to-1 against" style odds into a probability
probability_to_odds(probability)	(1 - p) / p; 0.0 if probability is within 1e-30 of zero
jackpot_ev_threshold(jackpot_odds, ticket_cost)	odds * cost — the jackpot must exceed this figure for the jackpot line of the bet, taken alone, to have non-negative expected value

Two real lottery formats, worked end to end. A standard "6/49" game draws 6 numbers from 49 with no separate bonus pool: combinations(49, 6) = 13,983,816, so lottery_odds(49, 6, 1) returns the same number — the real-world 1-in-14-million odds quoted for that format. Powerball draws 5 white balls from 69 and one red Powerball from a separate pool of 26: lottery_odds(69, 5, 26) = combinations(69, 5) * 26 = 11,238,513 * 26 = 292,201,338, which is Powerball's actual published jackpot odds, not a rounded stand-in. Feeding that figure into jackpot_ev_threshold(292201338.0, 2.0) returns 584,402,676: at a $2 ticket price, the jackpot alone would need to exceed roughly $584.4 million before the jackpot line of the bet is +EV — and that threshold ignores the smaller prize tiers, tax withholding, and the chance of splitting the jackpot with another winner, each of which raises the number a bettor should actually compare a real jackpot to.

The annuity is worth less than the lump sum's nominal total, on purpose. A jackpot advertised as a $1,000,000/year for 20 years annuity is not "worth $20,000,000 today" — discounting matters. annuity_vs_lump_sum(50000.0, 20.0, 0.05) (a $50,000/year, 20-year annuity discounted at 5%) returns 623,110.52, well under the 1,000,000 a naive payment * years would give; that undiscounted figure is exactly what the function falls back to when discount_rate is passed as 0.0, confirmed by annuity_vs_lump_sum(50000.0, 20.0, 0.0) = 1,000,000.0.

numismatics: what a coin is worth beyond its metal

Function	What it does
melt_value(metal_weight_oz, metal_price_per_oz)	weight * price — the coin's value if melted down for raw metal, nothing else
premium_over_melt(market_price, melt_val)	(market - melt) / melt * 100; 0.0 if melt_val is within 1e-30 of zero
grade_numeric_to_scale(sheldon_grade)	Identity, clamped to [0, 70] — the Sheldon Scale numismatic dealers and grading services (PCGS, NGC) use, where 70 is a theoretically perfect coin
population_scarcity(certified_count, total_mintage)	certified / mintage * 100; 0.0 if total_mintage is within 1e-30 of zero
annual_return(purchase_price, current_price, years)	((current/purchase)^(1/years) - 1) * 100; 0.0 if purchase_price or years is within 1e-30 of zero, or if the price ratio is non-positive
insurance_value(market_price, coverage_factor)	price * factor
mintage_per_capita(mintage, population)	mintage / population; 0.0 if population is within 1e-30 of zero
survival_rate_estimate(surviving, original_mintage)	surviving / original * 100; 0.0 if original_mintage is within 1e-30 of zero
price_trend(prices_avg_old, prices_avg_new)	(new - old) / old * 100; 0.0 if prices_avg_old is within 1e-30 of zero
auction_fee(hammer_price, buyer_premium_pct)	hammer * premium / 100
total_cost_acquisition(hammer, premium, tax_rate)	hammer * (1 + premium/100) * (1 + tax_rate/100) — buyer's premium and sales tax compound on top of each other, not add
bullion_content_troy_oz(weight_grams, purity)	weight * purity / 31.1035 — grams of pure metal converted to troy ounces using the real gram-per-troy-ounce constant

Grading and scarcity are two independent axes. grade_numeric_to_scale says nothing about how many other coins share that grade — a coin graded 65 (grade_numeric_to_scale(65.0) = 65.0, well inside the clamp) could still be population_scarcity(500.0, 1000000.0) = 0.05 percent of its original mintage, or the majority of survivors at that grade; the module keeps these as two separate scalars precisely because a high Sheldon grade and true rarity are different, independently-priced properties of a coin, not two readings of the same underlying quality.

Total acquisition cost is multiplicative, and that changes the answer. For a $10,000 hammer price at a 20% buyer's premium and 8% sales tax, total_cost_acquisition(10000.0, 20.0, 8.0) returns 12,960.0 — i.e. 10000 * 1.20 * 1.08 — not 10000 * (1 + 0.20 + 0.08) = 12,800.0. The 160-dollar gap is tax being charged on the premium as well as the hammer price, which is how most auction houses actually invoice a sale; a caller that additively approximates the two surcharges will under-quote the true cost on every purchase.

Bullion content uses the real conversion constant, not a rounded one. 31.1035 grams per troy ounce is precise to five significant figures (the commonly rounded 31.1 would misprice a large holding); bullion_content_troy_oz(31.1035, 1.0) = 1.0 exactly recovers one troy ounce from one gram-for-gram-equivalent input, and bullion_content_troy_oz(8.0, 0.9) (an 8-gram coin at 90% purity) returns 0.23151 troy ounces of pure metal.

museum: conservation physics and collections ratios

Function	What it does
lux_hours_annual(lux_level, hours_per_day, days_per_year)	lux * hours_per_day * days_per_year — cumulative annual light dose, the standard unit conservators budget a fragile object's total light exposure in
uv_exposure(uv_mw_per_lumen, lux)	uv_ratio * lux, in mW/m²
relative_humidity_from_temps(dry_bulb, wet_bulb)	Simplified RH ≈ 100 - 5*(dry_bulb - wet_bulb) (%); the docstring is explicit this is a linear approximation, not the psychrometric chart
moisture_content_equilibrium(rh, temperature)	Simplified EMC ≈ rh*0.15 + temperature*0.01 (%)
time_weighted_preservation_index(twpi_ref, temp, rh)	Simplified TWPI ≈ ref * exp(-0.05*(temp-20) - 0.02*(rh-50)) — decays exponentially away from a 20°C / 50% RH reference point
light_damage_reciprocity(intensity, duration)	intensity * duration — the reciprocity law: a given cumulative dose does the same damage whether delivered as low light for a long time or bright light briefly
climate_stability_score(temp_variation, rh_variation)	100 - 2*ΔT - ΔRH — higher is a more stable environment
storage_density(items, floor_area)	items / floor_area; 0.0 if floor_area <= 0
collection_growth_rate(items_added, items_deaccessioned, total)	(added - removed) / total * 100 (%); 0.0 if total <= 0
insurance_value_appreciation(initial_value, annual_rate, years)	initial * (1 + rate)^years, evaluated as exp(years * log(1+rate))
visitor_density(visitors, gallery_area)	visitors / gallery_area; 0.0 if gallery_area <= 0
conservation_priority_score(condition, significance, risk)	condition*0.4 + significance*0.3 + risk*0.3 — a fixed weighting, condition counted more heavily than either significance or risk

Every conservation formula here is a documented simplification, not a metrology-grade instrument reading. The module's own docstring flags relative_humidity_from_temps, moisture_content_equilibrium, and time_weighted_preservation_index as approximations (≈, not =); a real psychrometric RH calculation depends on barometric pressure and a lookup table, and real equilibrium-moisture-content and TWPI models (the Image Permanence Institute's actual TWPI, for one) are considerably more involved than a single exponential in two variables. These functions are useful for the ordering and rough sizing of a conservation decision — is this gallery's climate more stable than that one's, is this display case's light dose over budget — not for a certificate-grade environmental report.

A worked light-dose and appreciation example, pinned in the test suite. A gallery lit at 50 lux for 8 hours a day, 365 days a year: lux_hours_annual(50.0, 8.0, 365.0) = 146,000 lux-hours, the annual dose a conservator compares against a material-specific budget (paper and textiles typically get a far lower annual allowance than oil paintings). Separately, a $1,000,000 collection appreciating at 5% a year for a decade: insurance_value_appreciation(1000000.0, 0.05, 10.0) returns 1,628,894.63 — the exact compound-growth figure, not a linear 50%-over-ten-years approximation, which would understate it by over $128,000.

storage_density and visitor_density are the same ratio shape over different denominators. Both divide a count by an area and both return 0.0 rather than dividing by zero when the area is non-positive, but they answer different questions: storage_density tracks how tightly objects are packed into storage (a collections-management concern), while visitor_density tracks how crowded a gallery is (a conservation and visitor-experience concern, since foot traffic raises humidity and vibration). Neither function calls the other, and nothing in museum computes one from the other.

sports_analytics: baseball, football, Elo, and basketball, four unrelated scoring systems in one module

Function	What it does
batting_average(hits, at_bats)	H / AB; 0.0 if at_bats == 0
on_base_percentage(hits, walks, hbp, at_bats, sf)	(H + BB + HBP) / (AB + BB + HBP + SF); 0.0 if the denominator is 0
slugging_percentage(total_bases, at_bats)	TB / AB; 0.0 if at_bats == 0
ops(obp, slg)	OBP + SLG — takes the two already-computed rates, does not recompute them
era(earned_runs, innings_pitched)	(ER / IP) * 9; 0.0 if innings_pitched == 0
whip(walks, hits, innings_pitched)	(BB + H) / IP; 0.0 if innings_pitched == 0
passer_rating_nfl(comp, att, yards, td, interceptions)	The standard simplified NFL formula: four components (completion rate, yards/attempt, TD rate, interception rate), each clamped to [0, 2.375], averaged and scaled by 100; 0.0 if att == 0
pythagorean_wins(runs_scored, runs_allowed, exponent)	RS^e / (RS^e + RA^e) — Bill James's win-expectation formula; 1.0 if runs_allowed <= 0 (and runs_scored > 0), 0.0 if runs_scored <= 0
elo_expected_score(rating_a, rating_b)	1 / (1 + 10^((Rb - Ra)/400))
elo_new_rating(old_rating, k, actual_score, expected_score)	R + K*(S - E)
true_shooting_percentage(points, fga, fta)	PTS / (2*(FGA + 0.44*FTA)); 0.0 if the denominator is 0
player_efficiency_rating_approx(points, rebounds, assists, steals, blocks, turnovers, minutes, pace)	Simplified PER: ((PTS+REB+AST+STL+BLK-TO) / MIN) * (pace/100); 0.0 if minutes == 0

passer_rating_nfl reproduces the real formula's exact perfect-game ceiling. Feeding it a maximal line — passer_rating_nfl(30.0, 30.0, 500.0, 5.0, 0.0), 30-for-30 passing (100% completion), 500 yards, 5 touchdowns, 0 interceptions — pushes all four intermediate components (a, b, c, d) past their 2.375 ceiling, where each gets clamped back down to exactly 2.375. Averaging four copies of 2.375 and scaling by 100 gives (2.375*4)/6*100 = 158.333..., which is the NFL's own well-known perfect passer rating of 158.3 — this module's "simplified" formula (its own docstring's word) reproduces the real ceiling exactly, even though a non-maximal game line will diverge from the NFL's official computation in the low decimals.

pythagorean_wins's zero-handling is asymmetric on purpose. A team that has scored but allowed nothing (pythagorean_wins(100.0, 0.0, 2.0) = 1.0) is a perfect team by construction — the formula never has to compute 0^e in the denominator, because the function checks runs_allowed <= 0 before touching the exponentiation. A team that has allowed runs but scored none returns 0.0 by the same short-circuit. Only when both are positive does the function fall through to the real exp(exponent * log(x)) formulation — for a team that has scored 800 runs and allowed 700 at the classical exponent of 2.0, pythagorean_wins(800.0, 700.0, 2.0) = 640000 / 1130000 = 0.566372, meaning that run differential alone predicts roughly a .566 winning percentage.

Elo's two functions are a pure function and a state update, and they compose in one call. Two equally-rated players show elo_expected_score(1500.0, 1500.0) = 0.5 — a coin flip, as Elo requires of an even match. A 200-point favorite's edge is elo_expected_score(1600.0, 1400.0) = 0.7597 (a plausible large-but-not-overwhelming favorite). Feeding that expectation into the update function for a player who won as the expected favorite — elo_new_rating(1500.0, 32.0, 1.0, 0.5) = 1500 + 32*(1 - 0.5) = 1516.0 — is the exact chain a real rating system runs after every game: expected score first, then the K-factor-scaled correction against the actual result.

Verification notes

The four mojo/tests/libs/test_*.mojo files behind this page (test_lottery.mojo, test_museum.mojo, test_numismatics.mojo, test_sports_analytics.mojo) are auto-generated coverage smoke tests: every function is called once with the literal argument 0.5 (or 0.5 repeated for multi-argument functions, 5/1 for the few Int-typed lottery parameters), and the only assertion is that the result is finite — none of them pin a specific expected number. The real numeric coverage lives in four separate hand-authored files under tests/mojo/: test_wave90.mojo (lottery, 26 pinned assertions including the real 6/49 and Powerball odds), test_wave83.mojo (museum, 24 pinned assertions), test_wave94.mojo (numismatics, 25 pinned assertions), and test_wave77.mojo (sports_analytics, assertions covering every function in the module). Every worked figure given above traces to one of those four files or was independently recomputed in plain Python from the formula in the corresponding mojo/engine/*.mojo source and matched to the value the wave test already asserts.

Grepping gitbook/ for museum, numismatics, lottery, and sports_analytics outside this page turns up nothing but the machine-generated runtime library catalog — no other page documents these four modules, and none of the four imports or is imported by a module documented elsewhere in this corpus.

Next: see Runtime library catalog for every module and function, or Mixture-of-experts and routing kernels for another page grouping several small, formula-only modules that don't share code but are too small to stand alone.