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Share of voice and competitor benchmarking for basketball organisations

Basketball organisations compete for attention across league news desks, team channels, creator ecosystems, and broadcast highlight culture. Share of voice without competitor context misleads commercial and communications planning.

Dantani builds SOV views that normalise entity mentions across channels and compare clubs, leagues, and partner brands on like-for-like windows. Eagle Eye entity linking feeds the counts; Sports PR and commercial teams consume the benchmarks.

A viral game-winner can dominate social while conference rivals still own wire share. Desks that stare at a single channel invent a false hierarchy. Cross-channel, competitor-relative SOV is the honest view.

Motivation

Raw mention counts favour large markets and viral moments. Communications leaders need peer benchmarks: how a club tracks against conference rivals during playoffs, draft week, or coaching changes. League offices need the same discipline across franchises when evaluating calendar density.

Partner brands sitting on jersey and court inventory ask a sharper question: are we adjacent to the team narrative, or invisible next to a louder rival's campaign week? Dantani answers with channel-normalised, competitor-set SOV—not with invented media-value currency.

Methodology

Entity-linked mentions are weighted by channel class—wire, digital news, social, and creator—then normalised within the configured competitor set for each class. Competitor sets are configured per organisation rather than global defaults. A mid-market club does not benchmark against the loudest global franchise by accident.

Windows align to basketball reality: regular-season slices, playoff series, draft week, free agency, and coaching-change fortnights. Broadcast-adjacent commentary (talk shows, podcasts with transcripts) enters as its own class when licensed feeds exist.

SOV is a share, not a vanity total. Dantani never converts schematic shares into fabricated currency or engagement scores. Charts stay illustrative when used for architecture discussion.

Schematic SOV by channel class
Schematic / illustrativeNewsSocialCreatorOrg AOrg BOrg C
Illustrative share-of-voice comparison across three hypothetical basketball orgs. Schematic values only; not measured production KPIs. Broadcast-adjacent commentary enters when licensed feeds exist.

Desk workflow

  • Configure competitor set with commercial and comms leads before the window opens.
  • Pin channel weights for the review (partner reviews often weight news higher than meme social).
  • Run mid-window checks after viral games so narratives do not wait for month-end.
  • Export series-relative views for ownership or league meetings without inventing currency.
  • Reconcile partner-adjacent SOV against campaign calendars, not against unrelated league noise.

Koba narrates which competitors moved and on which channels—prose for non-technical stakeholders, still grounded in the same SOV objects commercial teams review.

Schemas and code

Illustrative pseudocode

type ChannelClass = "news" | "social" | "creator" | "broadcast_adj";

type SovWindow = {
  orgId: string;
  competitors: string[];
  start: string;
  end: string;
  channelWeights: Record<ChannelClass, number>;
};

type SovCell = {
  orgId: string;
  channel: ChannelClass;
  mentions: number;
  addressable: number; // competitor-set mention total for this channel
};

Illustrative pseudocode

def competitor_set_sov(mentions, competitors, channels):
    # Competitor-set SOV: org share within the configured set, per channel.
    # Denominator is sum of competitor mentions in that channel — not global
    # channel_totals outside the set.
    out = {}
    for ch in channels:
        denom = sum(mentions[org][ch] for org in competitors)
        for org in competitors:
            cell = mentions[org][ch]
            share = cell / denom if denom else 0.0
            out.setdefault(org, {})[ch] = {
                "mentions": cell,
                "addressable": denom,
                "sov": share,
            }
    return out

def weighted_sov(sov_cells, weights):
    return {
        org: sum(sov_cells[org][ch]["sov"] * weights[ch] for ch in weights)
        for org in sov_cells
    }

Failure modes

  • Global competitor defaults: mid-market clubs crushed by mega-franchise baselines.
  • Unnormalised social: meme volume masquerades as strategic share.
  • Entity bleed: player mentions attributed to wrong franchise after trades.
  • Window mismatch: comparing a playoff series to a quiet regular-season week.
  • Partner double-count: brand and team marks counted twice in the same piece.
  • Currency conversion: turning shares into invented media value destroys trust.

Illustrative basketball patterns

  • Playoff series windows where rival SOV flips after a viral game-winner.
  • Draft-week creator volume that outpaces traditional news for young stars.
  • Franchise relocation or arena stories that dominate civic news more than league wires.
  • Partner brand SOV adjacent to team marks during jersey and court campaigns.
  • Coaching-change fortnights where talk-radio transcripts outweigh short-form social.

Where Dantani products fit

Sports PR and commercial teams share the same SOV objects with different weights. Monitoring supplies the entity-linked mentions; board packs reuse those series without minting vanity currency.

Partner and league reviews

Partner reviews go cleaner when SOV objects carry channel weights agreed in advance. Commercial teams often upweight news and broadcast-adjacent classes; community teams upweight creator. The same underlying mentions support both views without rewriting history.

League offices comparing franchises should fix windows to calendar events—All-Star, draft, free agency—rather than arbitrary months. That discipline keeps schematic benchmarks interpretable in ownership meetings without inventing currency.

Takeaways

Useful SOV is competitive and channel-normalised. Dantani delivers basketball benchmarks that communications and commercial teams can defend in partner reviews without inventing vanity accuracy scores.

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