Datasets:

riteshnaik
/

GameTideData

# Engine settings, keyed by assumption ID (docs/assumptions.md).
# This is the machine-readable form of the assumptions the core engine uses.
# Reference tables live beside this file: chart_of_accounts.csv, departments.csv,
# price_book.csv and states.csv (sales tax rates, G12).

master_seed: 20240101

A1: {history_start: 2020-01-01, founded: 2020-01-06, first_sale: 2021-01-04}
A2: {generated_end: 2029-12-31}
A3: {release_day_after_close: 15}
A5: {time_zone: America/New_York}
A9: {close_business_day: 8}

B1:  # active customers at January 1, 2024 (a check on the history, not an input: the book is built from 2021)
  small_club: 110
  high_school: 105
  district: 25
  college: 85
  pro: 25
B2:  # average ARR at start, dollars
  small_club: 6000
  high_school: 12000
  district: 45000
  college: 70000
  pro: 120000
B3: {2021: 80, 2022: 220, 2023: 365, 2024: 400, 2025: 390, 2026: 450, 2027: 510, 2028: 560, 2029: 610}
B4: {2021: 2000000, 2022: 6500000, 2023: 12100000, 2024: 15000000, 2025: 16500000, 2026: 20000000, 2027: 24000000, 2028: 28000000, 2029: 32600000}
B5:  # new logos by year and segment (2027 high schools include 40 through the state association, E21)
  2021: {small_club: 25, high_school: 20, district: 5, college: 26, pro: 7}
  2022: {small_club: 48, high_school: 38, district: 12, college: 38, pro: 11}
  2023: {small_club: 52, high_school: 44, district: 16, college: 38, pro: 13}
  2024: {small_club: 38, high_school: 25, district: 4, college: 15, pro: 6}
  2025: {small_club: 18, high_school: 12, district: 2, college: 12, pro: 4}
  2026: {small_club: 28, high_school: 15, district: 3, college: 22, pro: 20}
  2027: {small_club: 32, high_school: 45, district: 3, college: 12, pro: 6}
  2028: {small_club: 34, high_school: 25, district: 4, college: 18, pro: 8}
  2029: {small_club: 36, high_school: 30, district: 5, college: 20, pro: 9}
B6: {small_club: {2021: 0.35, 2023: 0.30, 2024: 0.25}}
B7: {high_school: {2021: 0.15, 2023: 0.125, 2024: 0.10, 2025: 0.18, 2026: 0.13, 2027: 0.11, 2028: 0.10}}
B8: {district: {2021: 0.09, 2023: 0.075, 2024: 0.06}}
B9: {college: {2021: 0.12, 2023: 0.10, 2024: 0.08, 2025: 0.10, 2026: 0.07, 2027: 0.07, 2028: 0.08}}
B10: {pro: {2021: 0.075, 2023: 0.0625, 2024: 0.05}}
B11: {2023: 2, 2024: 3, 2025: 3, 2026: 15, 2027: 6, 2028: 4, 2029: 4}
B12: {min: 3, max: 10, mean: 6}
B14: {nc_share: 0.04}
B16:  # lead sport mix and sports licensed per customer
  small_club: {mix: {Soccer: 0.45, Basketball: 0.20, Volleyball: 0.15, Baseball and Softball: 0.10, Hockey: 0.10}, sports: 1.0}
  high_school: {mix: {Football: 0.45, Basketball: 0.25, Soccer: 0.10, Baseball and Softball: 0.10, Volleyball: 0.10}, sports: 1.5}
  district: {mix: {Football: 0.70, Basketball: 0.20, Soccer: 0.10}, sports: 4.0}
  college: {mix: {Football: 0.65, Basketball: 0.35}, sports: 6.0}
  pro: {mix: {Soccer: 0.40, Basketball: 0.20, Hockey: 0.20, Baseball and Softball: 0.20}, sports: 1.0}
B17: {small_club_exempt_share: 0.30}
B20:  # average first-year ARR of a new logo, dollars, by segment, and a yearly deal-size index
  small_club: 4500
  high_school: 9000
  district: 28000
  college: 38000
  pro: 65000
  index_min: 0.90
  index_max: 1.90
  plan_factor: {2022: 0.92, 2029: 0.80}  # calibration: realized wins run ahead of plan in these years

C1:  # term mix: months -> share
  small_club: {12: 1.0}
  high_school: {12: 0.85, 24: 0.15}
  district: {12: 0.50, 36: 0.50}
  college: {12: 0.55, 24: 0.25, 36: 0.20}
  pro: {12: 0.40, 24: 0.30, 36: 0.30}
C2:  # billing frequency
  small_club: monthly
  high_school: annual
  district: {annual: 0.40, semiannual: 0.60}
  college: {annual: 0.30, semiannual: 0.70}
  pro: {annual: 0.70, quarterly: 0.30}
C3: {escalator: 0.05}
C12: {renewal_opportunity_days_before_end: 120}

D1:  # expansion ARR as a share of opening ARR (target), and the expansion intensity tuned to reach it
  target: {2022: 0.15, 2023: 0.20, 2024: 0.15, 2025: 0.11, 2026: 0.17, 2027: 0.16, 2028: 0.14, 2029: 0.12}
  nrr: {2022: 1.00, 2023: 1.07, 2024: 1.05, 2025: 0.95, 2026: 1.07, 2027: 1.07, 2028: 1.04, 2029: 1.03}
  intensity: {2021: 1.20, 2022: 1.22, 2023: 2.05, 2024: 2.05, 2025: 1.50, 2026: 2.30, 2027: 1.98, 2028: 1.40, 2029: 1.20}
D2: {high_school: 0.12, district: 0.25, college: 0.35, small_club: 0.03, pro: 0.03}
D3: {high_school: 0.05, district: 0.08, college: 0.06}
D4: {Essentials: 0.12, Professional: 0.18, Elite: 0.25, mid_term_share: 0.33}
D5:  # launched modules: monthly adoption among eligible teams
  GT-MOD-NIL: {from: 2025-04-01, monthly: {2025: 0.014, 2026: 0.014, 2027: 0.009, 2028: 0.007}}
  GT-MOD-LOAD: {from: 2026-04-06, monthly: {2026: 0.012, 2027: 0.006, 2028: 0.006}}
  GT-MOD-SCOUT: {from: 2028-01-10, monthly: {2028: 0.017}}
D6: {essentials_to_professional: 0.08, professional_to_elite: 0.03}
D7: {professional_to_essentials: 0.03, high_school_2025: 0.10, elite_to_professional: 0.02}
D8: {swap_share: 0.04}
D9: {seat_add_annual: 0.20}
D10: {seat_reduction_at_renewal: 0.10}
D11: {default: 0.03, high_school_2026: 0.12, other_2026: 0.04, low: 0.10, high: 0.20}
D12:
  annual_list_increase_from_2026: 0.03
  annual_list_increase_before_2024: 0.03
  price_change_2025: {GT-TIER-ESS: 0.15, GT-MOD-LIVE: 0.20}

G2:  # payment terms in days
  smb_terms: 30
  negotiated: {30: 0.85, 60: 0.12, 90: 0.03}
G3:  # days paid after due date: [low, high, share over 30 days late]
  small_club: card
  high_school: [15, 45, 0.35]
  district: [30, 60, 0.50]
  college: [0, 20, 0.10]
  pro: [0, 15, 0.08]
  long_tail: {high_school: 0.03, district: 0.05, college: 0.02, pro: 0.01}  # share of invoices paid 91 to 150 days late
C8: {termination_for_convenience_share: {college: 0.05, pro: 0.05, high_school: 0.20, district: 0.20}}
C17: {suspend_days_past_due: 90}
G4: {first_attempt_decline: 0.04, retry_success: 0.75, retry_days: 7, nonpay_monthly: 0.001}
G5: {card_percent: 0.029, card_fixed_cents: 30, ach_percent: 0.008, ach_cap_cents: 500}
G6: {payout_business_days: 2}
G8: {short_pay_districts: 8, short_pay_low: 0.01, short_pay_high: 0.03}
G10:  # allowance rates by days past due
  current: 0.005
  d1_30: 0.02
  d31_60: 0.05
  d61_90: 0.15
  over_90: 0.40
  public_school_factor: 0.5
  small_club_factor: 1.5
G11: {churning_last_invoice_unpaid: {default: 0.15, 2025: 0.25}, write_off_days: 120}
G13: {filing_day: 20}

H1: {method: daily_proration_posted_monthly}
H13: {accrual_reverses_on_day: 1}
H14: {performance_factor: {2020: 0.00, 2021: 0.00, 2022: 0.80, 2023: 1.00, 2024: 0.90, 2025: 0.60, 2026: 0.90, 2027: 1.10, 2028: 0.85, 2029: 1.00}, payout_day: 15}
H15:  # audit adjustments, dated December 31 and posted in March
  2024: {commission_period_error_months: 12, unaccrued_december_bills: 90000}
  2025: {missed_prepaid: 40000}
  2028: {lease_remeasurement_timing: 30000}
H5:  # hardware orders: monthly order chance per customer and the mix (sized to the lifecycle model's hardware share)
  monthly_order: {small_club: 0.020, high_school: 0.045, district: 0.060, college: 0.070, pro: 0.080}
  fall_factor: 1.8
  scale: {2020: 0.0, 2021: 0.0, 2022: 0.43, 2023: 0.46, 2024: 0.47, 2025: 0.51, 2026: 0.30, 2027: 0.38, 2028: 0.52, 2029: 0.56}
  from: 2022-03-01
H6: {ship_days: [5, 15], install_days: [14, 28]}
H7: {return_rate: 0.02, bad_lot_return_rate: 0.06}
H8: {warranty_rate: 0.03}
H9: {cover_weeks: 8, sensor_build_units: 350}
H10: {capitalizable_engineering_share: 0.40, early_share: {2020: 0.0, 2021: 0.08, 2022: 0.18, 2023: 0.27}}
H4:
  implementation_essentials: 500
  essentials_attach: 0.30
  implementation_pro: 2500
  implementation_pro_extra_team: 500
  implementation_elite: 10000
  analyst_rate: 150
  analyst_share: {college: 0.16, pro: 0.28, district: 0.04}
  analyst_hours: {college: [8, 24], pro: [12, 36], district: [4, 12]}
  training_share: 0.03
  training_day: 1500
  year_factor: {2021: 0.50, 2022: 0.55, 2023: 0.65, 2024: 0.78, 2025: 0.84, 2026: 0.79, 2027: 0.82, 2028: 0.82, 2029: 0.85}
  implementation_factor: {2021: 0.35, 2022: 0.50, 2023: 0.65}  # design-partner and launch discounts on implementation
H4b: {usage_share: 0.12, usage_mean: 230, usage_growth: {2023: 0.8, 2024: 1.0, 2025: 1.3, 2026: 1.5, 2027: 1.7, 2028: 1.9, 2029: 2.1}, from: 2023-01-01}

I1: {source: departments.csv}
I2:  # headcount waypoints (month-end dates); hiring fills toward the line between them
  2020-02-29: 0
  2020-03-31: 3
  2020-12-31: 7
  2021-12-31: 26
  2022-12-31: 58
  2023-06-30: 73
  2023-12-31: 78
  2024-01-31: 78
  2024-12-31: 88
  2025-10-31: 100
  2025-12-31: 98
  2026-01-31: 97
  2026-02-28: 92
  2026-12-31: 97
  2027-12-31: 108
  2028-12-31: 117
  2029-12-31: 128
I3: {net_hires_2024: 10, planned_net_hires_2024: 15, planned_net_hires: {2023: 20, 2024: 15, 2025: 10, 2026: -1, 2027: 11, 2028: 9, 2029: 11}}
L1:  # board budget: prior-year month by account and department, grown by these plan rates; 2024 sits above its own outcome (E2)
  plan_rates:
    revenue: {2023: 2.10, 2024: 1.53, 2025: 1.24, 2026: 1.12, 2027: 1.18, 2028: 1.17, 2029: 1.17}
    cogs: {2023: 1.90, 2024: 1.40, 2025: 1.20, 2026: 1.10, 2027: 1.14, 2028: 1.14, 2029: 1.12}
    people: {2023: 1.70, 2024: 1.24, 2025: 1.22, 2026: 1.02, 2027: 1.10, 2028: 1.09, 2029: 1.10}
    other: {2023: 1.50, 2024: 1.20, 2025: 1.15, 2026: 1.00, 2027: 1.06, 2028: 1.06, 2029: 1.06}
    nonop: {2023: 1.0, 2024: 1.0, 2025: 1.0, 2026: 1.0, 2027: 1.0, 2028: 1.0, 2029: 1.0}
  budget_2024_over_outcome: {revenue: 1.03, cogs: 1.02, people: 1.05, other: 1.04, nonop: 1.0}
I4:  # voluntary attrition, annual
  sales: 0.28  # before the April 2026 pay reset (I26)
  customer_facing: 0.15
  engineering_product: 0.10
  ga: 0.08
I9: {median_days_to_fill: 60}
I12:  # median base salary by role, dollars
  SDR: 51000
  SMB AE: 70000
  Mid-market AE: 84000
  Enterprise AE: 112000
  Sales manager: 126000
  Solutions consultant: 115000
  Sales operations: 82000
  Marketing operations: 78000
  CSM: 74000
  Support specialist: 51000
  Implementation consultant: 73000
  Hardware technician: 51000
  Cloud engineer: 130000
  Software engineer: 126000
  Senior engineer: 153000
  Product manager: 130000
  Designer: 102000
  Data scientist: 126000
  Engineering manager: 175000
  Operations lead: 92000
  Marketing manager: 88000
  Accountant: 73000
  Finance manager: 112000
  Recruiter: 70000
  IT specialist: 74000
  Counsel: 172000
  Vice president: 195000
  CEO: 256000
I12b: {founder_pay: {start: 0.45, seed: 0.65, series_a: 0.85}, band_index: {2020: 0.92, 2021: 0.95, 2022: 1.00, 2023: 1.00}}
I11: {agency_share: 0.20, agency_fee: 0.20, job_boards_monthly: {2020: 300, 2021: 1000, 2022: 3000}}
I13: {sdr_commission: 20000, csm_share: 0.15}
I14: {individual: 0.10, manager: 0.15, executive: 0.30}
I15: {2020: 0.0, 2021: 0.030, 2022: 0.040, 2023: 0.040, 2024: 0.035, 2025: 0.020, 2026: 0.030, 2027: 0.035, 2028: 0.030, 2029: 0.030}
I16: {first_pay_date: 2020-01-10, frequency_days: 14}
I17:
  ss_rate: 0.062
  medicare_rate: 0.0145
  futa_rate: 0.006
  futa_wage_base: 7000
  nc_sui_rate: 0.010
  ss_wage_base: {2020: 137700, 2021: 142800, 2022: 147000, 2023: 160200, 2024: 168600, 2025: 176100, 2026: 184500, 2027: 191700, 2028: 199200, 2029: 206700}
  nc_sui_wage_base: {2020: 25200, 2021: 26000, 2022: 28000, 2023: 29600, 2024: 31400, 2025: 32600, 2026: 33800, 2027: 35000, 2028: 36200, 2029: 37400}
I18:
  nc_withholding: {2020: 0.0525, 2021: 0.0525, 2022: 0.0499, 2023: 0.0475, 2024: 0.045, 2025: 0.0425, 2026: 0.0399, 2027: 0.0399, 2028: 0.0399, 2029: 0.0399}
  federal_standard_deduction: {2020: 12400, 2021: 12550, 2022: 12950, 2023: 13850, 2024: 14600, 2025: 15000, 2026: 16100, 2027: 16500, 2028: 16900, 2029: 17300}
I19: {health_employer_monthly: 750, health_employee_monthly: 150, k401_match: 0.04, k401_employee_deferral: 0.05}
I20: {share_of_headcount: 0.05, monthly_cost: 7500}
I21: {annual_sbc_share_of_salary: {individual: 0.045, manager: 0.08, executive: 0.20, commission: 0.035, csm: 0.035}}
I23: {weeks_per_year: 2, minimum_weeks: 4}
I25: {first_lead_at: 5, reports_per_lead: 9}  # team leads and reporting lines
I26: {date: 2026-04-01, market_adjustment: 0.08, sales_attrition_after: 0.16}  # the 2026 sales pay reset

J1: {dev_floor_monthly: {2020: 1500, 2021: 4000, 2022: 7000, 2023: 9000}, per_active_team_monthly: 42.0, per_video_hour_stored_monthly: 0.02, avg_stored_hours_per_team: 1000, per_livestream_viewing_hour: 0.05}
J2: {non_production_share: 0.20}
J3: {royalty_rate: 0.30, annual_minimum: 150000}
J4: {monthly: 10000, from: 2022-01-01}
J5:  # software tools: named systems adopted on their dates, the rest as headcount grows (one per headcount_per_tool people)
  tools: 45
  per_seat_monthly: 35
  headcount_per_tool: 1.7
  adoption: {Slack: 2020-01-06, Google Workspace: 2020-01-06, GitHub: 2020-01-06, Figma: 2020-01-06, Rippling: 2020-01-06,
             Carta: 2020-01-06, Atlassian: 2020-07-01, Tempo: 2020-07-01, Salesforce: 2020-07-01, Zoom: 2020-03-16,
             Amplitude: 2021-07-01, Avalara: 2022-01-03, Greenhouse: 2022-01-03, Sage Intacct: 2023-01-02,
             Anaplan: 2023-01-02, Gainsight: 2023-01-02, Kantata: 2023-01-02, Zylo: 2023-07-03}
  quickbooks: {start: 2020-01-06, end: 2022-12-31, annual: 2400}
J1b: {unit_cost_decline: 0.12, unit_cost_decline_from: 2024, commitment_discount_from_2025: 0.15, streaming_factor_from_q4_2024: 2.0, scout_processing_per_team_monthly: 40}
J6: {per_employee_monthly: {2020: 0, 2021: 0, 2022: 0, 2023: 20, 2024: 40, 2025: 40, 2026: 40, 2027: 60, 2028: 60, 2029: 60}}
J7: {sq_ft: 15000, rate_2022: 28.0, annual_increase: 0.03, lease_start: 2022-01-01, lease_end: 2029-12-31, discount_rate: 0.07,
     signed: 2021-10-15, deposit: 60000, build_out: 600000, furniture: 250000, coworking_per_person_monthly: 450}
J8: {annual_premium: 180000, renewal_month: 4, annual_increase: 0.05, early_premium: {2020: 12000, 2021: 35000, 2022: 85000, 2023: 140000}}
J9: {annual: 120000, months: [1, 2, 3]}
J10: {monthly: {2020: 3000, 2021: 5000, 2022: 8000, 2023: 10000, 2024: 12000}}
J17: {advisory_monthly_from_2025: 20000, annual_increase: 0.05}
J11: {per_employee_monthly: 350, high_travel_factor: 3.0, cut_q4_2025: 0.30, year_factor: {2020: 0.15, 2021: 0.45, 2022: 0.85, 2023: 1.0}}
J12: {card_share: 0.70}
J13: {terms_days: 30, payment_weekday: 3, late_share: 0.02}
J14: {basis: headcount}
J15: {freight_share: 0.04}
J16: {utilities_monthly: 4500, office_per_employee_monthly: 40, training_per_employee_monthly: 50, dues_monthly: 1500, licenses_monthly: 2000, telecom_per_employee_monthly: 30}
F1: {2020: 0.0, 2021: 0.10, 2022: 0.03, 2023: 0.105, 2024: 0.088, 2025: 0.130, 2026: 0.123, 2027: 0.112, 2028: 0.116, 2029: 0.160}
F1b: {floor_monthly: {2020: 2000, 2021: 23000, 2022: 10000}}
F2: {google: 0.55, meta: 0.25, linkedin: 0.20}
F3: {google: 3.50, meta: 1.20, linkedin: 8.00}
F5: {sessions_monthly: 25000, annual_growth: 0.08, month_factor: [0.85, 0.95, 1.15, 1.20, 1.20, 1.15, 1.05, 1.00, 0.95, 0.85, 0.80, 0.75]}

M1: {common_stock: 10000, founder_contribution: 50000, raises: {2020-03-16: 1500000, 2021-06-15: 4000000, 2022-04-12: 10000000}, lifecycle_model_cash_2023: 8214328}
M2: {limit: 5000000, opened: 2024-03-01, unused_fee: 0.0025, covenant_min_cash: 2000000, margin: 0.03, benchmark: {2024: 0.053, 2025: 0.043, 2026: 0.037, 2027: 0.035, 2028: 0.035, 2029: 0.035}}
M3: {draws: {2025-10-15: 3000000}, repayments: {2026-12-15: 1000000, 2027-12-15: 2000000}}
M4: {rate: {2020: 0.003, 2021: 0.001, 2022: 0.015, 2023: 0.040, 2024: 0.035}, threshold: 1000000}
M6: {monthly: {2020: 150, 2021: 400, 2022: 900, 2023: 1200, 2024: 1500}}

S:  # system adoption: the first month each system folder is published (system-scope.md); QuickBooks Online ends 2022-12-31
  accounting_qbo: 2020-01-01
  hr: 2020-03-01
  payroll: 2020-03-01
  bank: 2020-01-01
  equity: 2020-03-01
  cloud_costs: 2020-01-01
  engineering_time: 2020-07-01
  crm: 2020-07-01
  payments: 2021-01-01
  support: 2021-03-01
  product_usage: 2021-07-01
  marketing: 2022-01-01
  web_analytics: 2022-01-01
  ads: 2022-01-01
  commissions: 2022-01-01
  sales_tax: 2022-01-01
  recruiting: 2022-01-01
  accounting: 2023-01-01
  planning: 2023-01-01
  customer_success: 2023-01-01
  implementation: 2023-01-01
  inventory: 2023-01-01
  software_spend: 2023-07-01

Q1:
  stages: [Qualify, Discover, Demo, Proposal, Negotiate]
  probability: {Qualify: 10, Discover: 20, Demo: 40, Proposal: 60, Negotiate: 80, Closed Won: 100, Closed Lost: 0}
  forecast_category: {Qualify: Pipeline, Discover: Pipeline, Demo: Best Case, Proposal: Best Case, Negotiate: Commit, Closed Won: Closed, Closed Lost: Omitted}
Q2: {small_club: 0.30, high_school: 0.25, district: 0.22, college: 0.22, pro: 0.18, change_2025: -0.05}
Q3: {small_club: 21, high_school: 45, district: 120, college: 90, pro: 150}
Q9: {Price: 0.30, Competitor: 0.25, No decision: 0.20, Timing or budget: 0.15, Champion left: 0.10}
Q7: {default: 0.25, 2025: 0.35}
Q12: {lead_to_mql: 0.35, mql_to_sql: 0.40, sql_to_opportunity: 0.70}
Q22: {lost: 0.30, slip: 0.40}
Q23: {reassign_days: 10}
Q18: {steps: [0.0, 0.25, 0.50, 0.75, 0.90]}
Q17: {quarterly_phasing: [0.23, 0.28, 0.37, 0.12], role_ratio: {SMB AE: 0.75, Mid-market AE: 1.0, Enterprise AE: 1.25}}  # share of the annual quota in each quarter, following the bookings season (E1)
Q21: {planned_attainment: {2021: 0.90, 2022: 1.00, 2023: 1.00, 2024: 0.95, 2025: 0.80, 2026: 0.95, 2027: 1.00, 2028: 0.95, 2029: 0.95}, quota_floor: 0.60, floor_exempt_years: [2024]}
Q25: {new_business: 0.10, expansion: 0.05, renewal: 0.02}
Q26: {multiplier: 1.5}
Q27: {days: 182}
Q30: {per_qualified_opportunity: 200}
Q29: {amortization_months: 60}

N15: {share: 0.08}

K1:  # share of licensed seats active in season, and the engagement level behind it
  small_club: 0.60
  high_school: 0.65
  district: 0.55
  college: 0.75
  pro: 0.85
  off_season_factor: 0.5
  spread: 0.28
  yearly_drift: 0.12
  champion_left_factor: 0.65
  e10_factor: 0.75
  elite_seats_per_team: 150
K2: {hours_per_team_season: {Essentials: 150, Professional: 600, Elite: 2000}}
K3: {viewing_hours_per_broadcast: 180, growth_from_q4_2024: 1.6, overage_broadcast_share: 0.6}
K4: {threshold: 0.40, churn_factor: 2.0, low_usage_reason_share: 0.5, slope: 3.0, min_multiplier: 0.3, max_multiplier: 4.0}
K5: {per_team_monthly: 0.12, e10_factor: 3.0, score_baseline: 0.03}
K6: {first_response_business_hours: 4, resolution_business_days: 3, breach_normal: 0.08, breach_e10: 0.35}
K7: {usage: 0.40, support: 0.20, engagement: 0.20, payment: 0.20, green: 70, yellow: 50}
K8:  # NPS surveys: month-end of each survey and the target score
  response_share: 0.35
  targets: {2024-03-31: 40, 2024-10-31: 40, 2025-03-31: 38, 2025-10-31: 22, 2026-03-31: 30, 2026-10-31: 38, 2027-03-31: 43, 2027-10-31: 45, 2028-03-31: 44, 2028-10-31: 36, 2029-03-31: 41, 2029-10-31: 44}
N21: {fold_monthly: {default: 0.002, 2025: 0.008}}
K9: {normal_error_rate: 0.005, e10_error_rate: 0.02}

T1:  # tolerances; the first two years of revenue are small, so timing moves the ratios more
  default_tolerance: 0.05
  early_years: {2021: {revenue: 0.12, operating_margin: 0.10}, 2022: {revenue: 0.08, operating_margin: 0.05}}
  gross_margin_points: {default: 0.02, 2025: 0.03}

LM:  # lifecycle model targets by year (models/gametide-lifecycle-model.xlsx; tests/test_full_run.py checks they match)
  revenue: {2021: 1150000, 2022: 4944167, 2023: 10757000, 2024: 15548625, 2025: 18112500, 2026: 21017917, 2027: 25373333, 2028: 30030000, 2029: 34958625}
  gross_margin: {2021: 0.5348, 2022: 0.635, 2023: 0.6869, 2024: 0.7377, 2025: 0.7411, 2026: 0.7445, 2027: 0.7479, 2028: 0.7512, 2029: 0.755}
  operating_margin: {2021: -1.7696, 2022: -0.865, 2023: -0.4331, 2024: -0.1623, 2025: -0.2089, 2026: -0.0655, 2027: 0.0079, 2028: 0.0612, 2029: 0.085}
  rule_of_40: {2021: -1.7696, 2022: 1.385, 2023: 0.4284, 2024: 0.0774, 2025: -0.1089, 2026: 0.1466, 2027: 0.2079, 2028: 0.2278, 2029: 0.2493}
  avg_headcount: {2021: 16.03, 2022: 44.11, 2023: 71.47, 2024: 81.38, 2025: 95.82, 2026: 94.97, 2027: 103.66, 2028: 112.62, 2029: 124.06}
  closing_cash: {2021: 3271050, 2022: 10578090, 2023: 8214328, 2024: 7231757, 2025: 7744927, 2026: 7202320, 2027: 7540185, 2028: 11869658, 2029: 17869435}
  closing_arr: {2021: 2000000, 2022: 6500000, 2023: 12100000, 2024: 15000000, 2025: 16500000, 2026: 20000000, 2027: 24000000, 2028: 28000000, 2029: 32600000}
  nrr: {2022: 1, 2023: 1.07, 2024: 1.05, 2025: 0.98, 2026: 1.07, 2027: 1.07, 2028: 1.04, 2029: 1.03}
  grr: {2022: 0.85, 2023: 0.87, 2024: 0.9, 2025: 0.87, 2026: 0.9, 2027: 0.91, 2028: 0.9, 2029: 0.91}
  new_arr: {2021: 2000000, 2022: 4500000, 2023: 5145000, 2024: 2295000, 2025: 1800000, 2026: 2345000, 2027: 2600000, 2028: 3040000, 2029: 3760000}
  subscription_revenue: {2021: 1000000, 2022: 4250000, 2023: 9300000, 2024: 13550000, 2025: 15750000, 2026: 18250000, 2027: 22000000, 2028: 26000000, 2029: 30300000}

# Headline event parameters (assumptions section O)
O:
  E4: {signed: 2024-03-15, start: 2024-04-01, end: 2025-06-30, discount: 0.25, schools: 12}
  E8: {date: 2025-07-01, schools_staying: 4, staying_share: 0.65}
  E9: {date: 2025-07-01, contraction: 20000}
  E10: {date: 2025-09-08, affected: 60, churn_factor: 2.0, credit_share: 0.06, expansion_factor_q4: 0.5, fix_contractors_monthly: 60000, fix_months: [2025-09, 2026-01]}
  E13: {start: 2025-11-03, end: 2026-01-31}
  E15: {date: 2026-02-15, roles: 8}
  E16: {first_half_2026_share: 0.8}
  E17: {launch: 2026-04-06, build_start: 2026-01-05, bad_lot_month: 2026-07}
  E18: {net90_customers: 1}
  E20: {program_id: MIG-2027-BUNDLE, renewal_share: 0.55, new_share: 0.45, after_2027_share: 0.30}
  E21: {signed: 2027-03-01, schools: 40, margin: 0.20}
  E22: {from_close_of: 2027-07-31, business_day: 5}
  E25: {detected: 2028-05-08, costs: 250000, recovery: 150000, recovery_date: 2028-11-15, small_club_churn_points: 0.03}
  E26: {date: 2028-08-14, followers: 2, new_leader_start: 2028-10-02, q3_loss_share: 0.10}
  E27: {date: 2028-11-01, years: 5, rate: 33.0}
  E29: {start: 2024-07-01, end: 2025-06-30, district_reduction_share: 0.15}
  E30: {from: 2026-01-01, discount: 0.10, switch_share: 0.42, churn_factor: 0.33}
# CRM people and activity (decided 2026-10-19: build the CRM objects the data dictionary lists)
Q14: {new_won: [16, 30], new_lost: [8, 15], other_deal: [3, 8], lead_touches: [3, 7], csm_touch_per_month: 0.6, business_review_segments: [district, college, pro], pre_churn_days: 120, pre_churn_factor: 0.5, meeting_share: 0.2}
Q31: {per_customer: {small_club: [1, 2], high_school: [2, 3], district: [3, 5], college: [3, 5], pro: [3, 6]}, per_prospect: [1, 2], per_child_account: 1}
Q32: {second_version_share: 0.6, first_version_discount_share: 0.5, valid_days: 30}
Q33: {submission_weekday: 0, commit_judgment: [0.85, 1.10], run_rate_share: 0.8}
# Active users and the engagement survey (decided 2026-10-19)
K10: {dau_share: 0.20, wau_share: 0.55}
I24: {first_year: 2022, base: 80, attrition_weight: 1.2, rif_drop: 9, participation: [0.72, 0.92], min_group: 5}