How Fieldglass Ranks Candidates: A Supplier's Guide to the Match

SAP Fieldglass ranks submitted candidates with a resume assessment algorithm that matches the skills on a candidate’s resume against the application’s skills library, weighs industry experience, work experience, and qualifications, and produces a banded score shown as a badge from Best Match down to No Match. The hiring manager sees those badges before they read a single word, so the ranking, not the CV, is often what decides who gets reviewed first.

For a staffing supplier that is the whole game. You are not submitting into an inbox where a recruiter reads in the order received. You are submitting into a scoring engine that sorts your candidate against every other agency’s candidate the moment you hit submit. Understanding how that score is built is the difference between landing at the top of the buyer’s list and sitting three screens down where nobody scrolls.

How the resume assessment algorithm scores a candidate

Fieldglass runs every submission through the same sequence. According to SAP’s own documentation, the resume assessment algorithm matches the skills listed on the resume against the application’s skills library, analyzes industry experience, work experience, and qualifications based on the Resume Assessment Weighting the buyer has configured, and produces a banded score.

The important thing to notice is that the score is relative and configurable. It is not a fixed pass mark that every candidate meets or fails. The buyer decides how much each factor counts, and your candidate is banded against the others in the same requisition. A profile that would look excellent for one posting can band poorly for another that weights industry experience heavily, even when the underlying CV is identical.

The four factors Fieldglass weighs

The Resume Assessment Weighting field lets the buyer distribute importance across four categories. Every submission is scored on the same four, but the mix changes per client and sometimes per requisition.

Factor	What Fieldglass reads	What the supplier controls
Skills	Named skills on the resume matched to the buyer’s skills library	Whether the candidate’s skills are named the way the library expects
Industry experience	Sectors and domains the candidate has worked in	Making sector relevance explicit rather than implied
Work experience	Length and relevance of employment history	Clear roles, dates, and scope on every entry
Qualifications	Certifications, education, and required credentials	Listing the exact qualifications the posting asks for

The word to underline is matched. Fieldglass is not reading your candidate’s CV the way a human recruiter does, inferring that a “Kubernetes specialist” obviously knows containers. It is comparing tokens against a library. If the posting’s library holds “Container Orchestration” and your CV says only “Kubernetes”, a naive match may miss the connection and cost the candidate points on the skills factor. This is why the way a CV is written, not only who the candidate is, moves the score.

What the match badges mean

Fieldglass turns the numeric score into a banded badge so a hiring manager can sort at a glance. The bands run from Best Match at the top through Better Match, Good Match, and Weak Match, down to No Match. These badges appear on the job seeker record, in card views, and in list views, and quick preview also shows a skills percentage and a related experience percentage indicating how closely the candidate fits the posting.

The practical consequence is brutal for suppliers who submit and hope. A hiring manager staring at forty submissions filters or sorts by badge and works top down. A Good Match candidate can be genuinely strong for the role and still never get read, because a handful of Better and Best Match badges sit above them. The ranking is a queue, and you want to enter it near the front.

Weighted qualification scoring: the qualifications-only mode

Some buyers switch on a stricter setting called Weighted Qualification Scoring. When it is enabled, SAP’s job seeker evaluation documentation states that only qualifications are used to score the candidate. Rate and availability are deliberately excluded from the calculated score, so a cheaper or sooner-available candidate gets no scoring advantage from those levers.

The mechanics are simple and unforgiving. If the posting defines three required qualifications, each one is worth a third of the score. Miss one and the candidate caps at roughly 67 percent before anything else is considered. There is no partial credit for being close, and there is no way to compensate a missing certification with a stronger work history, because history is not in the calculation under this mode.

How Fieldglass automates the ranking

SAP describes this as machine assisted rather than manual. Its documentation on machine learning features for candidate analysis says candidates are assessed by matching the skills they list on their resumes to those on the job description, scores are generated for how well skills and related experience align to the posting, and badges are then assigned automatically. Newer skills based hiring features push this further by extracting a structured skills taxonomy from the requisition itself and ranking submissions against it.

For the supplier the takeaway is not the underlying model, it is the input the model reads. The algorithm only ever sees the structured text of the CV you submitted and the profile fields you filled in. It cannot see the phone call where you vouched for the candidate, and it does not know the industry shorthand your desk uses. Everything that moves the score has to be legible in the document and the fields. This is the same discipline that decides whether a CV parser reads a candidate correctly in any ATS: structure and explicit naming beat clever prose every time.

What suppliers should actually do about it

Three habits move candidates up the Fieldglass ranking without any special access to the buyer’s configuration.

Mirror the posting’s language. Read the required skills and qualifications, then make sure the exact terms appear on the CV where the candidate genuinely has them. If the posting says “Stakeholder Management” and the CV says “worked closely with clients”, name the skill. This is not keyword stuffing, it is speaking the skills library’s dialect so the match connects.

Submit a complete, structured profile. Sparse work history and vague dates cost points on the work experience and industry factors. A CV where every role has a clear title, dates, and a line on scope gives the algorithm something to score. This is where a consistent template earns its keep. Reformatting each candidate into a clean, field rich layout before submission is exactly the kind of task an agency should automate with CV formatting rather than rebuild by hand for every requisition.

Score your own candidate before you submit. Compare the CV against the posting’s four factors yourself and fix the gaps you can honestly fix, meaning naming real skills and adding real qualifications the candidate holds but forgot to list. This is the manual version of what AI candidate matching does at scale: rank the fit, then decide. Doing it before Fieldglass does saves you from submitting a Weak Match you could have made a Better Match.

None of this changes who the candidate is. It changes whether Fieldglass can see who the candidate is. For the deeper mechanics of getting a submission accepted in the first place, see our guide to submitting a CV to SAP Fieldglass, and for how the ranking compares against the other major platform, our Beeline vs Fieldglass comparison. If you are new to selling into these systems at all, start with what a vendor management system is.

Frequently asked questions

How does SAP Fieldglass rank candidates?

Fieldglass runs each submitted resume through a resume assessment algorithm that matches the candidate’s listed skills against the buyer’s skills library and weighs industry experience, work experience, and qualifications according to the buyer’s configured weighting. It produces a banded score, shown as a badge from Best Match down to No Match, that hiring managers use to sort and review submissions.

What are the Fieldglass match badges?

The badges are banded labels that translate a candidate’s numeric assessment score into a quick visual signal. They run from Best Match at the top through Better Match, Good Match, and Weak Match, to No Match at the bottom, and they appear on the job seeker record and in card and list views. Quick preview also shows a skills percentage and a related experience percentage.

What is Weighted Qualification Scoring in Fieldglass?

It is an optional configuration that scores a candidate on their qualifications only, excluding rate and availability from the calculation. When the posting defines a set of required qualifications, each one carries an equal share of the score, so a missing certification directly caps how high the candidate can band. Buyers use it when credentials are non-negotiable.

Does Fieldglass use AI to rank candidates?

SAP’s documentation describes machine learning features that match resume skills to job description skills, generate alignment scores, and assign badges automatically, and newer skills based hiring features extract a skills taxonomy from the requisition to rank submissions against it. The scoring is algorithmic and configurable rather than a hidden black box, which means suppliers can influence it by writing CVs the algorithm can read.

How can a staffing supplier improve a candidate’s Fieldglass score?

Mirror the posting’s exact skill and qualification language on the CV where the candidate genuinely qualifies, submit a complete and structured profile with clear roles and dates, and check the candidate against the posting’s four factors before submitting. Because Fieldglass scores the text of the resume and the profile fields rather than the candidate’s real background, a well structured, explicitly named submission bands higher than a strong candidate described vaguely.